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For the IPDPS ParSocial event a presentation submission is required by 15th May. The event is on 3rd June. https://gist.github.com/wolfram77/51b15ca09eb28f6909673a2deb1a314d DYNAMIC BATCH PARALLEL ALGORITHMS FOR UPDATING PAGERANK Subhajit Sahut, Kishore Kothapallit and Dip Sankar Banerjeet tInternational Institute of Information Technology Hyderabad, India. tIndian Institute of Technology Jodhpur, India. subhajit.sahu@research. ,kkishore@iiit.ac.in, dipsankarb@iitj.ac.in This work is partially supported by a grant from the Department of Science and Technology (DST), India, under the National Supercomputing Mission (NSM) R&D in Exascale initiative vide Ref. No: DST/NSM/R&D Exascale/2021/16. FACEBOOK 15 TAKING A PAGE OUT OF GOOGLE’S PLAYBOOK 10 STOP FAKE NEWS FROM GOING VIRAL PUBLISHED APR 2015 BY SALVADOR RODRIGUEZ Click-Gap: When is Facebook is driving disproportionate amounts of traffic to websites. Effort to rid fakes news from Facebook’s services. Is a website relying on Facebook to drive significant traffic, but not well ranked by the rest of the web? Also News Citation Graph. PAGERANK APPLICATIONS Ranking of websites. Measuring scientific impact of researchers. Finding the best teams and athletes. Ranking companies by talent concentration. Predicting road/foot traffic in urban spaces. Analysing protein networks. Finding the most authoritative news sources Identifying parts of brain that change jointly. Toxic waste management. PAGERANK APPLICATIONS Debugging complex software systems (Moni torRank) Finding the most original writers (BookRank) Finding topical authorities (TwitterRank) WHAT IS PAGERANK l—-d Plu = Cus + —— UCIiNny Pru u->v = (1-—d) x “us ( ) outdegy, PageRank is a lLink-analysis algorithm. By Larry Page and Sergey Brin in 1996. For ordering information on the web. Represented with a random-surfer model. Rank of a page is defined recursively. Calculate iteratively with power-iteration.

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Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER

This paper presents two algorithms for efficiently computing PageRank on dynamically updating graphs in a batched manner: DynamicLevelwisePR and DynamicMonolithicPR. DynamicLevelwisePR processes vertices level-by-level based on strongly connected components and avoids recomputing converged vertices on the CPU. DynamicMonolithicPR uses a full power iteration approach on the GPU that partitions vertices by in-degree and skips unaffected vertices. Evaluation on real-world graphs shows the batched algorithms provide speedups of up to 4000x over single-edge updates and outperform other state-of-the-art dynamic PageRank algorithms.

RAPIDS cuGraph – Accelerating all your Graph needs

The relationships between data sets matter. Discovering, analyzing, and learning those relationships is a central part to expanding our understand, and is a critical step to being able to predict and act upon the data. Unfortunately, these are not always simple or quick tasks.
To help the analyst we introduce RAPIDS, a collection of open-source libraries, incubated by NVIDIA and focused on accelerating the complete end-to-end data science ecosystem. Graph analytics is a critical piece of the data science ecosystem for processing linked data, and RAPIDS is pleased to offer cuGraph as our accelerated graph library.
Simply accelerating algorithms only addressed a portion of the problem. To address the full problem space, RAPIDS cuGraph strives to be feature-rich, easy to use, and intuitive. Rather than limiting the solution to a single graph technology, cuGraph supports Property Graphs, Knowledge Graphs, Hyper-Graphs, Bipartite graphs, and the basic directed and undirected graph.
A Python API allows the data to be manipulated as a DataFrame, similar and compatible with Pandas, with inputs and outputs being shared across the full RAPIDS suite, for example with the RAPIDS machine learning package, cuML.
This talk will present an overview of RAPIDS and cuGraph. Discuss and show examples of how to manipulate and analyze bipartite and property graph, plus show how data can be shared with machine learning algorithms. The talk will include some performance and scalability metrics. Then conclude with a preview of upcoming features, like graph query language support, and the general RAPIDS roadmap.

Accelerating Data Science With GPUs

1) NVIDIA-Iguazio Accelerated Solutions for Deep Learning and Machine Learning (30 mins):
About the speaker:
Dr. Gabriel Noaje, Senior Solutions Architect, NVIDIA
http://bit.ly/GabrielNoaje
2) GPUs in Data Science Pipelines ( 30 mins)
- GPU as a Service for enterprise AI
- A short demo on the usage of GPUs for model training and model inferencing within a data science workflow
About the speaker:
Anant Gandhi, Solutions Engineer, Iguazio Singapore. https://www.linkedin.com/in/anant-gandhi-b5447614/

Narayanan Sundaram, Research Scientist, Intel Labs at MLconf SF - 11/13/15

GraphMat: Bridging the Productivity-Performance Gap in Graph Analytics: With increasing interest in large-scale distributed graph analytics for machine learning and data mining, more data scientists and developers are struggling to achieve high performance without sacrificing productivity on large graph problems. In this talk, I will discuss our solution to this problem: GraphMat. Using generalized sparse matrix-based primitives, we are able to achieve performance that is very close to hand-optimized native code, while allowing users to write programs using the familiar vertex-centric programming paradigm. I will show how we optimized GraphMat to achieve this performance on distributed platforms and provide programming examples. We have integrated GraphMat with Apache Spark in a manner that allows the combination to outperform all other distributed graph frameworks. I will explain the reasons for this performance and show that our approach achieves very high hardware efficiency in both single-node and distributed environments using primitives that are applicable to many machine learning and HPC problems. GraphMat is open source software and available for download.

Review on Multiply-Accumulate Unit

In present day MAC unit is demanded in most of the Digital signal processing. Function of addition and multiplication is performed by the MAC unit. MAC operates in two stages. Firstly, multiplier computes the given number output and the result is forwarded to second stage i.e. addition/accumulation operates. Speed of multiplier is important in MAC unit which determines critical path as well as area is also of great importance in designing of MAC unit. Multiplier plays an important roles in many digital signal processing (DSP) applications such as in convolution, digital filters and other data processing unit. Many research has been performed on MAC implementation. This paper provides analysis of the research and investigations held till now.

Big Graph : Tools, Techniques, Issues, Challenges and Future Directions

Analyzing interconnection structures among the data through the use of graph algorithms and
graph analytics has been shown to provide tremendous value in many application domains (like
social networks, protein networks, transportation networks, bibliographical networks,
knowledge bases and many more). Nowadays, graphs with billions of nodes and trillions of
edges have become very common. In principle, graph analytics is an important big data
discovery technique. Therefore, with the increasing abundance of large scale graphs, designing
scalable systems for processing and analyzing large scale graphs has become one of the
timeliest problems facing the big data research community. In general, distributed processing of
big graphs is a challenging task due to their size and the inherent irregular structure of graph
computations. In this paper, we present a comprehensive overview of the state-of-the-art to
better understand the challenges of developing very high-scalable graph processing systems. In
addition, we identify a set of the current open research challenges and discuss some promising
directions for future research.

BIG GRAPH: TOOLS, TECHNIQUES, ISSUES, CHALLENGES AND FUTURE DIRECTIONS

Analyzing interconnection structures among the data through the use of graph algorithms and
graph analytics has been shown to provide tremendous value in many application domains (like
social networks, protein networks, transportation networks, bibliographical networks, knowledge bases and many more). Nowadays, graphs with billions of nodes and trillions of
edges have become very common. In principle, graph analytics is an important big data
discovery technique. Therefore, with the increasing abundance of large scale graphs, designing scalable systems for processing and analyzing large-scale graphs has become one of the timeliest problems facing the big data research community. In general, distributed processing of big graphs is a challenging task due to their size and the inherent irregular structure of graph computations. In this paper, we present a comprehensive overview of the state-of-the-art to better understand the challenges of developing very high-scalable graph processing systems. In addition, we identify a set of the current open research challenges and discuss some promising
directions for future research.

Leveraging GPU-Accelerated Analytics on top of Apache Spark with Todd Mostak

There has been growing interest in harnessing the parallelism of Graphics Processing Units (GPUs) to accelerate analytics workloads. GPUs have become the standard platform for many machine learning algorithms, particularly in the field of deep neural networks (DNNs), while making increasing inroads into more traditional domains such as analytics databases and visual analytics. However there is a strong need to couple these new platforms with Apache Spark, which has emerged as the de facto analytics platform for data scientists. In this talk we discuss how we built a connector from Spark to the open source GPU-powered MapD Analytics Platform, and the use cases such a connector enables around being able to pull high value data from Spark and cache it on the GPU for subsequent interactive visual analysis and machine learning. We will conclude with a brief demo of an end-to-end Spark-to-MapD pipeline.

Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER

This paper presents two algorithms for efficiently computing PageRank on dynamically updating graphs in a batched manner: DynamicLevelwisePR and DynamicMonolithicPR. DynamicLevelwisePR processes vertices level-by-level based on strongly connected components and avoids recomputing converged vertices on the CPU. DynamicMonolithicPR uses a full power iteration approach on the GPU that partitions vertices by in-degree and skips unaffected vertices. Evaluation on real-world graphs shows the batched algorithms provide speedups of up to 4000x over single-edge updates and outperform other state-of-the-art dynamic PageRank algorithms.

RAPIDS cuGraph – Accelerating all your Graph needs

The relationships between data sets matter. Discovering, analyzing, and learning those relationships is a central part to expanding our understand, and is a critical step to being able to predict and act upon the data. Unfortunately, these are not always simple or quick tasks.
To help the analyst we introduce RAPIDS, a collection of open-source libraries, incubated by NVIDIA and focused on accelerating the complete end-to-end data science ecosystem. Graph analytics is a critical piece of the data science ecosystem for processing linked data, and RAPIDS is pleased to offer cuGraph as our accelerated graph library.
Simply accelerating algorithms only addressed a portion of the problem. To address the full problem space, RAPIDS cuGraph strives to be feature-rich, easy to use, and intuitive. Rather than limiting the solution to a single graph technology, cuGraph supports Property Graphs, Knowledge Graphs, Hyper-Graphs, Bipartite graphs, and the basic directed and undirected graph.
A Python API allows the data to be manipulated as a DataFrame, similar and compatible with Pandas, with inputs and outputs being shared across the full RAPIDS suite, for example with the RAPIDS machine learning package, cuML.
This talk will present an overview of RAPIDS and cuGraph. Discuss and show examples of how to manipulate and analyze bipartite and property graph, plus show how data can be shared with machine learning algorithms. The talk will include some performance and scalability metrics. Then conclude with a preview of upcoming features, like graph query language support, and the general RAPIDS roadmap.

Accelerating Data Science With GPUs

1) NVIDIA-Iguazio Accelerated Solutions for Deep Learning and Machine Learning (30 mins):
About the speaker:
Dr. Gabriel Noaje, Senior Solutions Architect, NVIDIA
http://bit.ly/GabrielNoaje
2) GPUs in Data Science Pipelines ( 30 mins)
- GPU as a Service for enterprise AI
- A short demo on the usage of GPUs for model training and model inferencing within a data science workflow
About the speaker:
Anant Gandhi, Solutions Engineer, Iguazio Singapore. https://www.linkedin.com/in/anant-gandhi-b5447614/

Narayanan Sundaram, Research Scientist, Intel Labs at MLconf SF - 11/13/15

GraphMat: Bridging the Productivity-Performance Gap in Graph Analytics: With increasing interest in large-scale distributed graph analytics for machine learning and data mining, more data scientists and developers are struggling to achieve high performance without sacrificing productivity on large graph problems. In this talk, I will discuss our solution to this problem: GraphMat. Using generalized sparse matrix-based primitives, we are able to achieve performance that is very close to hand-optimized native code, while allowing users to write programs using the familiar vertex-centric programming paradigm. I will show how we optimized GraphMat to achieve this performance on distributed platforms and provide programming examples. We have integrated GraphMat with Apache Spark in a manner that allows the combination to outperform all other distributed graph frameworks. I will explain the reasons for this performance and show that our approach achieves very high hardware efficiency in both single-node and distributed environments using primitives that are applicable to many machine learning and HPC problems. GraphMat is open source software and available for download.

Review on Multiply-Accumulate Unit

In present day MAC unit is demanded in most of the Digital signal processing. Function of addition and multiplication is performed by the MAC unit. MAC operates in two stages. Firstly, multiplier computes the given number output and the result is forwarded to second stage i.e. addition/accumulation operates. Speed of multiplier is important in MAC unit which determines critical path as well as area is also of great importance in designing of MAC unit. Multiplier plays an important roles in many digital signal processing (DSP) applications such as in convolution, digital filters and other data processing unit. Many research has been performed on MAC implementation. This paper provides analysis of the research and investigations held till now.

Big Graph : Tools, Techniques, Issues, Challenges and Future Directions

Analyzing interconnection structures among the data through the use of graph algorithms and
graph analytics has been shown to provide tremendous value in many application domains (like
social networks, protein networks, transportation networks, bibliographical networks,
knowledge bases and many more). Nowadays, graphs with billions of nodes and trillions of
edges have become very common. In principle, graph analytics is an important big data
discovery technique. Therefore, with the increasing abundance of large scale graphs, designing
scalable systems for processing and analyzing large scale graphs has become one of the
timeliest problems facing the big data research community. In general, distributed processing of
big graphs is a challenging task due to their size and the inherent irregular structure of graph
computations. In this paper, we present a comprehensive overview of the state-of-the-art to
better understand the challenges of developing very high-scalable graph processing systems. In
addition, we identify a set of the current open research challenges and discuss some promising
directions for future research.

BIG GRAPH: TOOLS, TECHNIQUES, ISSUES, CHALLENGES AND FUTURE DIRECTIONS

Analyzing interconnection structures among the data through the use of graph algorithms and
graph analytics has been shown to provide tremendous value in many application domains (like
social networks, protein networks, transportation networks, bibliographical networks, knowledge bases and many more). Nowadays, graphs with billions of nodes and trillions of
edges have become very common. In principle, graph analytics is an important big data
discovery technique. Therefore, with the increasing abundance of large scale graphs, designing scalable systems for processing and analyzing large-scale graphs has become one of the timeliest problems facing the big data research community. In general, distributed processing of big graphs is a challenging task due to their size and the inherent irregular structure of graph computations. In this paper, we present a comprehensive overview of the state-of-the-art to better understand the challenges of developing very high-scalable graph processing systems. In addition, we identify a set of the current open research challenges and discuss some promising
directions for future research.

Leveraging GPU-Accelerated Analytics on top of Apache Spark with Todd Mostak

There has been growing interest in harnessing the parallelism of Graphics Processing Units (GPUs) to accelerate analytics workloads. GPUs have become the standard platform for many machine learning algorithms, particularly in the field of deep neural networks (DNNs), while making increasing inroads into more traditional domains such as analytics databases and visual analytics. However there is a strong need to couple these new platforms with Apache Spark, which has emerged as the de facto analytics platform for data scientists. In this talk we discuss how we built a connector from Spark to the open source GPU-powered MapD Analytics Platform, and the use cases such a connector enables around being able to pull high value data from Spark and cache it on the GPU for subsequent interactive visual analysis and machine learning. We will conclude with a brief demo of an end-to-end Spark-to-MapD pipeline.

Big Stream Processing Systems, Big Graphs

Big Data, a recent phenomenon. Everyone talks about it, but do you really know what Big Data is? Join our four-part series about Big Data and you will get answers to your questions!
We will cover Introduction to Big Data and available platforms which we can use to deal with Big Data. And in the end, we are going to give you an insight into the possible future of dealing with Big Data.
After the two previous episodes you know the basics about Big Data. Yet, it might get a bit more complicated than that. Usually when you have to deal with data which is generated in real-time. In this case, you are dealing with Big Stream.
This episode of our series will be focussed on processing systems capable of dealing with Big Streams. But analysing data lacking graphical representation will not be very convenient for us. And this is where we have to use a platform capable of visualising Big Graphs. All these topics will be covered in today’s presentation.
#CHEDTEB
www.chedteb.eu

Nvidia gpu-application-catalog TESLA K80 GPU應用程式型錄

TESLA K80 GPU應用程式型錄
需購買相關應用軟體請上 http://www.appcenter.com.tw/ or http://www.cheerchain.com.tw/
購買請洽 祺荃企業有限公司-您可以信賴的軟體供應商
www.cheerchain.com.tw or www.appcenter.com.tw
Email : info@cheerchain.com.tw Phone : +8864-23863559

Venkatesh Ramanathan, Data Scientist, PayPal at MLconf ATL 2017

Large Scale Graph Processing & Machine Learning Algorithms for Payment Fraud Prevention:
PayPal is at the forefront of applying large scale graph processing and machine learning algorithms to keep fraudsters at bay. In this talk, I’ll present how advanced graph processing and machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention. I’ll elaborate on specific challenges in applying large scale graph processing & machine technique to payment fraud prevention. I’ll explain how we employ sophisticated machine learning tools – open source and in-house developed.
I will also present results from experiments conducted on a very large graph data set containing millions of edges and vertices.

Panel: NRP Science Impacts

The document discusses accelerating science discovery with AI inference-as-a-service. It describes showcases using this approach for high energy physics and gravitational wave experiments. It outlines the vision of the A3D3 institute to unite domain scientists, computer scientists, and engineers to achieve real-time AI and transform science. Examples are provided of using AI inference-as-a-service to accelerate workflows for CMS, ProtoDUNE, LIGO, and other experiments.

ISC Frankfurt 2015: Good, bad and ugly of accelerators and a complementary path

Accelerators Vs Adjoint Algorithmic Differentation (AAD).... NONSENSE. It is not a choice. The two can be combined to provide the ultimate accelerator. Accelerators such as NVIDIA GPUs, Intel Xeon Phis CAN be combined with AD. NAG has the software tools and expertise to deliver AD solutions for traditional architectures and accelerarors

Download It

The document discusses using Map-Reduce for machine learning algorithms on multi-core processors. It describes rewriting machine learning algorithms in "summation form" to express the independent computations as Map tasks and aggregating results as Reduce tasks. This formulation allows the algorithms to be parallelized efficiently across multiple cores. Specific machine learning algorithms that have been implemented or analyzed in this Map-Reduce framework are listed.

System mldl meetup

This document summarizes a presentation on Apache SystemML, an open source machine learning framework that provides scalable machine learning capabilities. It discusses SystemML's support for deep learning algorithms like convolutional neural networks and its ability to optimize machine learning workloads through techniques like operator fusion. It also demonstrates SystemML running image classification and medical image segmentation deep learning models on IBM Power systems and provides performance comparisons between Power and x86 architectures.

Presentation

The document discusses the future of computing platforms and how they will change to handle massive amounts of data and machine learning tasks. Some key points:
- Traditional views of performance gains from clock speed increases are over. New architectures enabled by multi-core CPUs will radically change computing.
- "Big data" tasks like search, machine learning, and real-time data analysis will be increasingly important drivers of new computing platforms.
- Simple machine learning models applied to massive amounts of data can produce useful results, even without deep domain expertise. This approach has been demonstrated to work well for tasks like language translation.
- Future platforms may blend CPUs and GPUs differently to best handle both serial and parallel tasks for big data and machine

Making Machine Learning Scale: Single Machine and Distributed

This document summarizes machine learning scalability from single machine to distributed systems. It discusses how true scalability is about how long it takes to reach a target accuracy level using any available hardware resources. It introduces GraphLab Create and SFrame/SGraph for scalable machine learning and graph processing. Key points include distributed optimization techniques, graph partitioning strategies, and benchmarks showing GraphLab Create can solve problems faster than other systems by using fewer machines.

How Data Volume Affects Spark Based Data Analytics on a Scale-up Server

Sheer increase in volume of data over the last decade has triggered research in cluster computing frameworks that enable web enterprises to extract big insights from big data. While Apache Spark is gaining popularity for exhibiting superior scale-out performance on the commodity machines, the impact of data volume on the performance of Spark based data analytics in scale-up configuration is not well under-stood. We present a deep-dive analysis of Spark based applications on a large scale-up server machine. Our analysis reveals that Spark based data analytics are DRAM bound and do not benefitt by using more than 12 cores for an executor. By enlarging input data size, application per-performance degrades significantly due to substantial increase in wait time during I/O operations and garbage collection, despite 10% better instruction retirement rate (due to lower L1 cache misses and higher core utilization). We match memory behavior with the garbage collector to improve performance of applications between 1.6x to 3x.

Rapids: Data Science on GPUs

In this deck from FOSDEM'19, Christoph Angerer from NVIDIA presents: Rapids - Data Science on GPUs.
"The next big step in data science will combine the ease of use of common Python APIs, but with the power and scalability of GPU compute. The RAPIDS project is the first step in giving data scientists the ability to use familiar APIs and abstractions while taking advantage of the same technology that enables dramatic increases in speed in deep learning. This session highlights the progress that has been made on RAPIDS, discusses how you can get up and running doing data science on the GPU, and provides some use cases involving graph analytics as motivation.
GPUs and GPU platforms have been responsible for the dramatic advancement of deep learning and other neural net methods in the past several years. At the same time, traditional machine learning workloads, which comprise the majority of business use cases, continue to be written in Python with heavy reliance on a combination of single-threaded tools (e.g., Pandas and Scikit-Learn) or large, multi-CPU distributed solutions (e.g., Spark and PySpark). RAPIDS, developed by a consortium of companies and available as open source code, allows for moving the vast majority of machine learning workloads from a CPU environment to GPUs. This allows for a substantial speed up, particularly on large data sets, and affords rapid, interactive work that previously was cumbersome to code or very slow to execute. Many data science problems can be approached using a graph/network view, and much like traditional machine learning workloads, this has been either local (e.g., Gephi, Cytoscape, NetworkX) or distributed on CPU platforms (e.g., GraphX). We will present GPU-accelerated graph capabilities that, with minimal conceptual code changes, allows both graph representations and graph-based analytics to achieve similar speed ups on a GPU platform. By keeping all of these tasks on the GPU and minimizing redundant I/O, data scientists are enabled to model their data quickly and frequently, affording a higher degree of experimentation and more effective model generation. Further, keeping all of this in compatible formats allows quick movement from feature extraction, graph representation, graph analytic, enrichment back to the original data, and visualization of results. RAPIDS has a mission to build a platform that allows data scientist to explore data, train machine learning algorithms, and build applications while primarily staying on the GPU and GPU platforms."
Learn more: https://rapids.ai/
and
https://fosdem.org/2019/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

NVIDIA Rapids presentation

This document summarizes a presentation by Dr. Christoph Angerer on RAPIDS, an open source library for GPU-accelerated data science. Some key points:
- RAPIDS provides an end-to-end GPU-accelerated workflow for data science using CUDA and popular tools like Pandas, Spark, and XGBoost.
- It addresses challenges with data movement and formats by keeping data on the GPU as much as possible using the Apache Arrow data format.
- Benchmarks show RAPIDS provides significant speedups over CPU for tasks like data preparation, machine learning training, and visualization.
- Future work includes improving cuDF (GPU DataFrame library), adding algorithms to cuML

APSys Presentation Final copy2

1) The document discusses implementing and evaluating deep neural networks (DNNs) on mainstream heterogeneous systems like CPUs, GPUs, and APUs.
2) Preliminary results show that an APU achieves the highest performance per watt compared to CPUs and GPUs for DNN models like MLP and autoencoders.
3) Data transfers between the CPU and GPU are identified as a bottleneck, but APUs can help avoid this issue through efficient data sharing and zero-copy techniques between the CPU and GPU.

Sigmaplot 13 PPT

StarCom Information Technology is a public company in India that aims to be a global provider of business intelligence, analytics, and big data solutions. It has formed a strategic partnership with a global analytics company to distribute their products in the Asia Pacific region. SigmaPlot is a graphing and data analysis software that helps scientists and researchers visualize and analyze data through various statistical tests and graph types. It provides an optimized interface and additional features in version 13 like forest plots, kernel density plots, and dot density graphs.

Phi Week 2019

The document discusses several pillars for national AI initiatives, including establishing AI centers of excellence, reskilling the workforce, and investing in key industries to drive growth and solve economic and social challenges. It also outlines different approaches for designing and optimizing AI systems, such as using GANs and GPU-accelerated simulations. Overall, the document promotes the development and application of AI through collaboration between universities, industry, and government.

GOAI: GPU-Accelerated Data Science DataSciCon 2017

The GPU Open Analytics Initiative, GOAI, is accelerating data science like never before. CPUs are not improving at the same rate as networking and storage, and leveraging GPUs data scientist can analyze more data than ever with less hardware. Learn more about how GPU are accelerating data science (not just Deep Learning), and how to get started.

GPU 101: The Beast In Data Centers

NVidia and Kinetica presented together about the trends in GPU use cases across industries. The basics and GPU architecture was discussed and how it compares with ASIC and FPGA.
Kinetica presented their In-Memory Database Platform powered by GPU which provides capabilities for fast analytics, geospatial analytics and realtime ML/Deep Learning execution engine.

AI and Deep Learning

This talk was presented in Startup Master Class 2017 - http://aaiitkblr.org/smc/ 2017 @ Christ College Bangalore. Hosted by IIT Kanpur Alumni Association and co-presented by IIT KGP Alumni Association, IITACB, PanIIT, IIMA and IIMB alumni.
My co-presenter was Biswa Gourav Singh. And contributor was Navin Manaswi.
http://dataconomy.com/2017/04/history-neural-networks/ - timeline for neural networks

Distributed computing abstractions_data_science_6_june_2016_ver_0.4

These slides outline the common distributed computing abstractions necessary to implement data science at scale. It starts with a characterization of the computations required to realize common machine learning at scale. Introductions to Hadoop MR, Spark, GraphLab are covered currently. Going forward, we shall update with Flink, Titan and TensorFlow and how to realize machine learning/deep learning algorithms on top of these frameworks as well as trade-offs between these frameworks.

RAPIDS – Open GPU-accelerated Data Science

RAPIDS – Open GPU-accelerated Data Science
RAPIDS is an initiative driven by NVIDIA to accelerate the complete end-to-end data science ecosystem with GPUs. It consists of several open source projects that expose familiar interfaces making it easy to accelerate the entire data science pipeline- from the ETL and data wrangling to feature engineering, statistical modeling, machine learning, and graph analysis.
Corey J. Nolet
Corey has a passion for understanding the world through the analysis of data. He is a developer on the RAPIDS open source project focused on accelerating machine learning algorithms with GPUs.
Adam Thompson
Adam Thompson is a Senior Solutions Architect at NVIDIA. With a background in signal processing, he has spent his career participating in and leading programs focused on deep learning for RF classification, data compression, high-performance computing, and managing and designing applications targeting large collection frameworks. His research interests include deep learning, high-performance computing, systems engineering, cloud architecture/integration, and statistical signal processing. He holds a Masters degree in Electrical & Computer Engineering from Georgia Tech and a Bachelors from Clemson University.

About TrueTime, Spanner, Clock synchronization, CAP theorem, Two-phase lockin...

TrueTime is a service that enables the use of globally synchronized clocks, with bounded error. It returns a time interval that is guaranteed to contain the clock’s actual time for some time during the call’s execution. If two intervals do not overlap, then we know calls were definitely ordered in real time. In general, synchronized clocks can be used to avoid communication in a distributed system.
The underlying source of time is a combination of GPS receivers and atomic clocks. As there are “time masters” in every datacenter (redundantly), it is likely that both sides of a partition would continue to enjoy accurate time. Individual nodes however need network connectivity to the masters, and without it their clocks will drift. Thus, during a partition their intervals slowly grow wider over time, based on bounds on the rate of local clock drift. Operations depending on TrueTime, such as Paxos leader election or transaction commits, thus have to wait a little longer, but the operation still completes (assuming the 2PC and quorum communication are working).

Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.

Big Stream Processing Systems, Big Graphs

Big Data, a recent phenomenon. Everyone talks about it, but do you really know what Big Data is? Join our four-part series about Big Data and you will get answers to your questions!
We will cover Introduction to Big Data and available platforms which we can use to deal with Big Data. And in the end, we are going to give you an insight into the possible future of dealing with Big Data.
After the two previous episodes you know the basics about Big Data. Yet, it might get a bit more complicated than that. Usually when you have to deal with data which is generated in real-time. In this case, you are dealing with Big Stream.
This episode of our series will be focussed on processing systems capable of dealing with Big Streams. But analysing data lacking graphical representation will not be very convenient for us. And this is where we have to use a platform capable of visualising Big Graphs. All these topics will be covered in today’s presentation.
#CHEDTEB
www.chedteb.eu

Nvidia gpu-application-catalog TESLA K80 GPU應用程式型錄

TESLA K80 GPU應用程式型錄
需購買相關應用軟體請上 http://www.appcenter.com.tw/ or http://www.cheerchain.com.tw/
購買請洽 祺荃企業有限公司-您可以信賴的軟體供應商
www.cheerchain.com.tw or www.appcenter.com.tw
Email : info@cheerchain.com.tw Phone : +8864-23863559

Venkatesh Ramanathan, Data Scientist, PayPal at MLconf ATL 2017

Large Scale Graph Processing & Machine Learning Algorithms for Payment Fraud Prevention:
PayPal is at the forefront of applying large scale graph processing and machine learning algorithms to keep fraudsters at bay. In this talk, I’ll present how advanced graph processing and machine learning algorithms such as Deep Learning and Gradient Boosting are applied at PayPal for fraud prevention. I’ll elaborate on specific challenges in applying large scale graph processing & machine technique to payment fraud prevention. I’ll explain how we employ sophisticated machine learning tools – open source and in-house developed.
I will also present results from experiments conducted on a very large graph data set containing millions of edges and vertices.

Panel: NRP Science Impacts

The document discusses accelerating science discovery with AI inference-as-a-service. It describes showcases using this approach for high energy physics and gravitational wave experiments. It outlines the vision of the A3D3 institute to unite domain scientists, computer scientists, and engineers to achieve real-time AI and transform science. Examples are provided of using AI inference-as-a-service to accelerate workflows for CMS, ProtoDUNE, LIGO, and other experiments.

ISC Frankfurt 2015: Good, bad and ugly of accelerators and a complementary path

Accelerators Vs Adjoint Algorithmic Differentation (AAD).... NONSENSE. It is not a choice. The two can be combined to provide the ultimate accelerator. Accelerators such as NVIDIA GPUs, Intel Xeon Phis CAN be combined with AD. NAG has the software tools and expertise to deliver AD solutions for traditional architectures and accelerarors

Download It

The document discusses using Map-Reduce for machine learning algorithms on multi-core processors. It describes rewriting machine learning algorithms in "summation form" to express the independent computations as Map tasks and aggregating results as Reduce tasks. This formulation allows the algorithms to be parallelized efficiently across multiple cores. Specific machine learning algorithms that have been implemented or analyzed in this Map-Reduce framework are listed.

System mldl meetup

This document summarizes a presentation on Apache SystemML, an open source machine learning framework that provides scalable machine learning capabilities. It discusses SystemML's support for deep learning algorithms like convolutional neural networks and its ability to optimize machine learning workloads through techniques like operator fusion. It also demonstrates SystemML running image classification and medical image segmentation deep learning models on IBM Power systems and provides performance comparisons between Power and x86 architectures.

Presentation

The document discusses the future of computing platforms and how they will change to handle massive amounts of data and machine learning tasks. Some key points:
- Traditional views of performance gains from clock speed increases are over. New architectures enabled by multi-core CPUs will radically change computing.
- "Big data" tasks like search, machine learning, and real-time data analysis will be increasingly important drivers of new computing platforms.
- Simple machine learning models applied to massive amounts of data can produce useful results, even without deep domain expertise. This approach has been demonstrated to work well for tasks like language translation.
- Future platforms may blend CPUs and GPUs differently to best handle both serial and parallel tasks for big data and machine

Making Machine Learning Scale: Single Machine and Distributed

This document summarizes machine learning scalability from single machine to distributed systems. It discusses how true scalability is about how long it takes to reach a target accuracy level using any available hardware resources. It introduces GraphLab Create and SFrame/SGraph for scalable machine learning and graph processing. Key points include distributed optimization techniques, graph partitioning strategies, and benchmarks showing GraphLab Create can solve problems faster than other systems by using fewer machines.

How Data Volume Affects Spark Based Data Analytics on a Scale-up Server

Sheer increase in volume of data over the last decade has triggered research in cluster computing frameworks that enable web enterprises to extract big insights from big data. While Apache Spark is gaining popularity for exhibiting superior scale-out performance on the commodity machines, the impact of data volume on the performance of Spark based data analytics in scale-up configuration is not well under-stood. We present a deep-dive analysis of Spark based applications on a large scale-up server machine. Our analysis reveals that Spark based data analytics are DRAM bound and do not benefitt by using more than 12 cores for an executor. By enlarging input data size, application per-performance degrades significantly due to substantial increase in wait time during I/O operations and garbage collection, despite 10% better instruction retirement rate (due to lower L1 cache misses and higher core utilization). We match memory behavior with the garbage collector to improve performance of applications between 1.6x to 3x.

Rapids: Data Science on GPUs

In this deck from FOSDEM'19, Christoph Angerer from NVIDIA presents: Rapids - Data Science on GPUs.
"The next big step in data science will combine the ease of use of common Python APIs, but with the power and scalability of GPU compute. The RAPIDS project is the first step in giving data scientists the ability to use familiar APIs and abstractions while taking advantage of the same technology that enables dramatic increases in speed in deep learning. This session highlights the progress that has been made on RAPIDS, discusses how you can get up and running doing data science on the GPU, and provides some use cases involving graph analytics as motivation.
GPUs and GPU platforms have been responsible for the dramatic advancement of deep learning and other neural net methods in the past several years. At the same time, traditional machine learning workloads, which comprise the majority of business use cases, continue to be written in Python with heavy reliance on a combination of single-threaded tools (e.g., Pandas and Scikit-Learn) or large, multi-CPU distributed solutions (e.g., Spark and PySpark). RAPIDS, developed by a consortium of companies and available as open source code, allows for moving the vast majority of machine learning workloads from a CPU environment to GPUs. This allows for a substantial speed up, particularly on large data sets, and affords rapid, interactive work that previously was cumbersome to code or very slow to execute. Many data science problems can be approached using a graph/network view, and much like traditional machine learning workloads, this has been either local (e.g., Gephi, Cytoscape, NetworkX) or distributed on CPU platforms (e.g., GraphX). We will present GPU-accelerated graph capabilities that, with minimal conceptual code changes, allows both graph representations and graph-based analytics to achieve similar speed ups on a GPU platform. By keeping all of these tasks on the GPU and minimizing redundant I/O, data scientists are enabled to model their data quickly and frequently, affording a higher degree of experimentation and more effective model generation. Further, keeping all of this in compatible formats allows quick movement from feature extraction, graph representation, graph analytic, enrichment back to the original data, and visualization of results. RAPIDS has a mission to build a platform that allows data scientist to explore data, train machine learning algorithms, and build applications while primarily staying on the GPU and GPU platforms."
Learn more: https://rapids.ai/
and
https://fosdem.org/2019/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

NVIDIA Rapids presentation

This document summarizes a presentation by Dr. Christoph Angerer on RAPIDS, an open source library for GPU-accelerated data science. Some key points:
- RAPIDS provides an end-to-end GPU-accelerated workflow for data science using CUDA and popular tools like Pandas, Spark, and XGBoost.
- It addresses challenges with data movement and formats by keeping data on the GPU as much as possible using the Apache Arrow data format.
- Benchmarks show RAPIDS provides significant speedups over CPU for tasks like data preparation, machine learning training, and visualization.
- Future work includes improving cuDF (GPU DataFrame library), adding algorithms to cuML

APSys Presentation Final copy2

1) The document discusses implementing and evaluating deep neural networks (DNNs) on mainstream heterogeneous systems like CPUs, GPUs, and APUs.
2) Preliminary results show that an APU achieves the highest performance per watt compared to CPUs and GPUs for DNN models like MLP and autoencoders.
3) Data transfers between the CPU and GPU are identified as a bottleneck, but APUs can help avoid this issue through efficient data sharing and zero-copy techniques between the CPU and GPU.

Sigmaplot 13 PPT

StarCom Information Technology is a public company in India that aims to be a global provider of business intelligence, analytics, and big data solutions. It has formed a strategic partnership with a global analytics company to distribute their products in the Asia Pacific region. SigmaPlot is a graphing and data analysis software that helps scientists and researchers visualize and analyze data through various statistical tests and graph types. It provides an optimized interface and additional features in version 13 like forest plots, kernel density plots, and dot density graphs.

Phi Week 2019

The document discusses several pillars for national AI initiatives, including establishing AI centers of excellence, reskilling the workforce, and investing in key industries to drive growth and solve economic and social challenges. It also outlines different approaches for designing and optimizing AI systems, such as using GANs and GPU-accelerated simulations. Overall, the document promotes the development and application of AI through collaboration between universities, industry, and government.

GOAI: GPU-Accelerated Data Science DataSciCon 2017

The GPU Open Analytics Initiative, GOAI, is accelerating data science like never before. CPUs are not improving at the same rate as networking and storage, and leveraging GPUs data scientist can analyze more data than ever with less hardware. Learn more about how GPU are accelerating data science (not just Deep Learning), and how to get started.

GPU 101: The Beast In Data Centers

NVidia and Kinetica presented together about the trends in GPU use cases across industries. The basics and GPU architecture was discussed and how it compares with ASIC and FPGA.
Kinetica presented their In-Memory Database Platform powered by GPU which provides capabilities for fast analytics, geospatial analytics and realtime ML/Deep Learning execution engine.

AI and Deep Learning

This talk was presented in Startup Master Class 2017 - http://aaiitkblr.org/smc/ 2017 @ Christ College Bangalore. Hosted by IIT Kanpur Alumni Association and co-presented by IIT KGP Alumni Association, IITACB, PanIIT, IIMA and IIMB alumni.
My co-presenter was Biswa Gourav Singh. And contributor was Navin Manaswi.
http://dataconomy.com/2017/04/history-neural-networks/ - timeline for neural networks

Distributed computing abstractions_data_science_6_june_2016_ver_0.4

These slides outline the common distributed computing abstractions necessary to implement data science at scale. It starts with a characterization of the computations required to realize common machine learning at scale. Introductions to Hadoop MR, Spark, GraphLab are covered currently. Going forward, we shall update with Flink, Titan and TensorFlow and how to realize machine learning/deep learning algorithms on top of these frameworks as well as trade-offs between these frameworks.

RAPIDS – Open GPU-accelerated Data Science

RAPIDS – Open GPU-accelerated Data Science
RAPIDS is an initiative driven by NVIDIA to accelerate the complete end-to-end data science ecosystem with GPUs. It consists of several open source projects that expose familiar interfaces making it easy to accelerate the entire data science pipeline- from the ETL and data wrangling to feature engineering, statistical modeling, machine learning, and graph analysis.
Corey J. Nolet
Corey has a passion for understanding the world through the analysis of data. He is a developer on the RAPIDS open source project focused on accelerating machine learning algorithms with GPUs.
Adam Thompson
Adam Thompson is a Senior Solutions Architect at NVIDIA. With a background in signal processing, he has spent his career participating in and leading programs focused on deep learning for RF classification, data compression, high-performance computing, and managing and designing applications targeting large collection frameworks. His research interests include deep learning, high-performance computing, systems engineering, cloud architecture/integration, and statistical signal processing. He holds a Masters degree in Electrical & Computer Engineering from Georgia Tech and a Bachelors from Clemson University.

Big Stream Processing Systems, Big Graphs

Big Stream Processing Systems, Big Graphs

Nvidia gpu-application-catalog TESLA K80 GPU應用程式型錄

Nvidia gpu-application-catalog TESLA K80 GPU應用程式型錄

Venkatesh Ramanathan, Data Scientist, PayPal at MLconf ATL 2017

Venkatesh Ramanathan, Data Scientist, PayPal at MLconf ATL 2017

Panel: NRP Science Impacts

Panel: NRP Science Impacts

ISC Frankfurt 2015: Good, bad and ugly of accelerators and a complementary path

ISC Frankfurt 2015: Good, bad and ugly of accelerators and a complementary path

Download It

Download It

System mldl meetup

System mldl meetup

Presentation

Presentation

Making Machine Learning Scale: Single Machine and Distributed

Making Machine Learning Scale: Single Machine and Distributed

How Data Volume Affects Spark Based Data Analytics on a Scale-up Server

How Data Volume Affects Spark Based Data Analytics on a Scale-up Server

Rapids: Data Science on GPUs

Rapids: Data Science on GPUs

NVIDIA Rapids presentation

NVIDIA Rapids presentation

APSys Presentation Final copy2

APSys Presentation Final copy2

Sigmaplot 13 PPT

Sigmaplot 13 PPT

Phi Week 2019

Phi Week 2019

GOAI: GPU-Accelerated Data Science DataSciCon 2017

GOAI: GPU-Accelerated Data Science DataSciCon 2017

GPU 101: The Beast In Data Centers

GPU 101: The Beast In Data Centers

AI and Deep Learning

AI and Deep Learning

Distributed computing abstractions_data_science_6_june_2016_ver_0.4

Distributed computing abstractions_data_science_6_june_2016_ver_0.4

RAPIDS – Open GPU-accelerated Data Science

RAPIDS – Open GPU-accelerated Data Science

About TrueTime, Spanner, Clock synchronization, CAP theorem, Two-phase lockin...

TrueTime is a service that enables the use of globally synchronized clocks, with bounded error. It returns a time interval that is guaranteed to contain the clock’s actual time for some time during the call’s execution. If two intervals do not overlap, then we know calls were definitely ordered in real time. In general, synchronized clocks can be used to avoid communication in a distributed system.
The underlying source of time is a combination of GPS receivers and atomic clocks. As there are “time masters” in every datacenter (redundantly), it is likely that both sides of a partition would continue to enjoy accurate time. Individual nodes however need network connectivity to the masters, and without it their clocks will drift. Thus, during a partition their intervals slowly grow wider over time, based on bounds on the rate of local clock drift. Operations depending on TrueTime, such as Paxos leader election or transaction commits, thus have to wait a little longer, but the operation still completes (assuming the 2PC and quorum communication are working).

Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.

Adjusting Bitset for graph : SHORT REPORT / NOTES

Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is commonly used for efficient graph computations. Unfortunately, using CSR for dynamic graphs is impractical since addition/deletion of a single edge can require on average (N+M)/2 memory accesses, in order to update source-offsets and destination-indices. A common approach is therefore to store edge-lists/destination-indices as an array of arrays, where each edge-list is an array belonging to a vertex. While this is good enough for small graphs, it quickly becomes a bottleneck for large graphs. What causes this bottleneck depends on whether the edge-lists are sorted or unsorted. If they are sorted, checking for an edge requires about log(E) memory accesses, but adding an edge on average requires E/2 accesses, where E is the number of edges of a given vertex. Note that both addition and deletion of edges in a dynamic graph require checking for an existing edge, before adding or deleting it. If edge lists are unsorted, checking for an edge requires around E/2 memory accesses, but adding an edge requires only 1 memory access.

Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...

Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.

Adjusting primitives for graph : SHORT REPORT / NOTES

Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).

Experiments with Primitive operations : SHORT REPORT / NOTES

This includes:
- Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
- Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
- Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
- Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).

PageRank Experiments : SHORT REPORT / NOTES

This includes:
- Adjusting data types for rank vector
- Adjusting Pagerank parameters
- Adjusting Sequential approach
- Adjusting OpenMP approach
- Comparing sequential approach
- Adjusting Monolithic (Sequential) optimizations (from STICD)
- Adjusting Levelwise (STICD) approach
- Comparing Levelwise (STICD) approach
- Adjusting ranks for dynamic graphs
- Adjusting Levelwise (STICD) dynamic approach
- Comparing dynamic approach with static
- Adjusting Monolithic CUDA approach
- Adjusting Monolithic CUDA optimizations (from STICD)
- Adjusting Levelwise (STICD) CUDA approach
- Comparing Levelwise (STICD) CUDA approach
- Comparing dynamic CUDA approach with static
- Comparing dynamic optimized CUDA approach with static

Algorithmic optimizations for Dynamic Monolithic PageRank (from STICD) : SHOR...

Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.

Adjusting OpenMP PageRank : SHORT REPORT / NOTES

For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).

word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...

Below are the important points I note from the 2020 paper by Martin Grohe:
- 1-WL distinguishes almost all graphs, in a probabilistic sense
- Classical WL is two dimensional Weisfeiler-Leman
- DeepWL is an unlimited version of WL graph that runs in polynomial time.
- Knowledge graphs are essentially graphs with vertex/edge attributes
ABSTRACT:
Vector representations of graphs and relational structures, whether handcrafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view.
Starting with a survey of embedding techniques that have been used in practice, in this paper we propose two theoretical approaches that we see as central for understanding the foundations of vector embeddings. We draw connections between the various approaches and suggest directions for future research.

DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES

https://gist.github.com/wolfram77/54c4a14d9ea547183c6c7b3518bf9cd1
There exist a number of dynamic graph generators. Barbasi-Albert model iteratively attach new vertices to pre-exsiting vertices in the graph using preferential attachment (edges to high degree vertices are more likely - rich get richer - Pareto principle). However, graph size increases monotonically, and density of graph keeps increasing (sparsity decreasing).
Gorke's model uses a defined clustering to uniformly add vertices and edges. Purohit's model uses motifs (eg. triangles) to mimick properties of existing dynamic graphs, such as growth rate, structure, and degree distribution. Kronecker graph generators are used to increase size of a given graph, with power-law distribution.
To generate dynamic graphs, we must choose a metric to compare two graphs. Common metrics include diameter, clustering coefficient (modularity?), triangle counting (triangle density?), and degree distribution.
In this paper, the authors propose Dygraph, a dynamic graph generator that uses degree distribution as the only metric. The authors observe that many real-world graphs differ from the power-law distribution at the tail end. To address this issue, they propose binning, where the vertices beyond a certain degree (minDeg = min(deg) s.t. |V(deg)| < H, where H~10 is the number of vertices with a given degree below which are binned) are grouped into bins of degree-width binWidth, max-degree localMax, and number of degrees in bin with at least one vertex binSize (to keep track of sparsity). This helps the authors to generate graphs with a more realistic degree distribution.
The process of generating a dynamic graph is as follows. First the difference between the desired and the current degree distribution is calculated. The authors then create an edge-addition set where each vertex is present as many times as the number of additional incident edges it must recieve. Edges are then created by connecting two vertices randomly from this set, and removing both from the set once connected. Currently, authors reject self-loops and duplicate edges. Removal of edges is done in a similar fashion.
Authors observe that adding edges with power-law properties dominates the execution time, and consider parallelizing DyGraph as part of future work.

Shared memory Parallelism (NOTES)

My notes on shared memory parallelism.
Shared memory is memory that may be simultaneously accessed by multiple programs with an intent to provide communication among them or avoid redundant copies. Shared memory is an efficient means of passing data between programs. Using memory for communication inside a single program, e.g. among its multiple threads, is also referred to as shared memory [REF].

A Dynamic Algorithm for Local Community Detection in Graphs : NOTES

**Community detection methods** can be *global* or *local*. **Global community detection methods** divide the entire graph into groups. Existing global algorithms include:
- Random walk methods
- Spectral partitioning
- Label propagation
- Greedy agglomerative and divisive algorithms
- Clique percolation
https://gist.github.com/wolfram77/b4316609265b5b9f88027bbc491f80b6
There is a growing body of work in *detecting overlapping communities*. **Seed set expansion** is a **local community detection method** where a relevant *seed vertices* of interest are picked and *expanded to form communities* surrounding them. The quality of each community is measured using a *fitness function*.
**Modularity** is a *fitness function* which compares the number of intra-community edges to the expected number in a random-null model. **Conductance** is another popular fitness score that measures the community cut or inter-community edges. Many *overlapping community detection* methods **use a modified ratio** of intra-community edges to all edges with atleast one endpoint in the community.
Andersen et al. use a **Spectral PageRank-Nibble method** which minimizes conductance and is formed by adding vertices in order of decreasing PageRank values. Andersen and Lang develop a **random walk approach** in which some vertices in the seed set may not be placed in the final community. Clauset gives a **greedy method** that *starts from a single vertex* and then iteratively adds neighboring vertices *maximizing the local modularity score*. Riedy et al. **expand multiple vertices** via maximizing modularity.
Several algorithms for **detecting global, overlapping communities** use a *greedy*, *agglomerative approach* and run *multiple separate seed set expansions*. Lancichinetti et al. run **greedy seed set expansions**, each with a *single seed vertex*. Overlapping communities are produced by a sequentially running expansions from a node not yet in a community. Lee et al. use **maximal cliques as seed sets**. Havemann et al. **greedily expand cliques**.
The authors of this paper discuss a dynamic approach for **community detection using seed set expansion**. Simply marking the neighbours of changed vertices is a **naive approach**, and has *severe shortcomings*. This is because *communities can split apart*. The simple updating method *may fail even when it outputs a valid community* in the graph.

Scalable Static and Dynamic Community Detection Using Grappolo : NOTES

A **community** (in a network) is a subset of nodes which are _strongly connected among themselves_, but _weakly connected to others_. Neither the number of output communities nor their size distribution is known a priori. Community detection methods can be divisive or agglomerative. **Divisive methods** use _betweeness centrality_ to **identify and remove bridges** between communities. **Agglomerative methods** greedily **merge two communities** that provide maximum gain in _modularity_. Newman and Girvan have introduced the **modularity metric**. The problem of community detection is then reduced to the problem of modularity maximization which is **NP-complete**. **Louvain method** is a variant of the _agglomerative strategy_, in that is a _multi-level heuristic_.
https://gist.github.com/wolfram77/917a1a4a429e89a0f2a1911cea56314d
In this paper, the authors discuss **four heuristics** for Community detection using the _Louvain algorithm_ implemented upon recently developed **Grappolo**, which is a parallel variant of the Louvain algorithm. They are:
- Vertex following and Minimum label
- Data caching
- Graph coloring
- Threshold scaling
With the **Vertex following** heuristic, the _input is preprocessed_ and all single-degree vertices are merged with their corresponding neighbours. This helps reduce the number of vertices considered in each iteration, and also help initial seeds of communities to be formed. With the **Minimum label heuristic**, when a vertex is making the decision to move to a community and multiple communities provided the same modularity gain, the community with the smallest id is chosen. This helps _minimize or prevent community swaps_. With the **Data caching** heuristic, community information is stored in a vector instead of a map, and is reused in each iteration, but with some additional cost. With the **Vertex ordering via Graph coloring** heuristic, _distance-k coloring_ of graphs is performed in order to group vertices into colors. Then, each set of vertices (by color) is processed _concurrently_, and synchronization is performed after that. This enables us to mimic the behaviour of the serial algorithm. Finally, with the **Threshold scaling** heuristic, _successively smaller values of modularity threshold_ are used as the algorithm progresses. This allows the algorithm to converge faster, and it has been observed a good modularity score as well.
From the results, it appears that _graph coloring_ and _threshold scaling_ heuristics do not always provide a speedup and this depends upon the nature of the graph. It would be interesting to compare the heuristics against baseline approaches. Future work can include _distributed memory implementations_, and _community detection on streaming graphs_.

Application Areas of Community Detection: A Review : NOTES

This is a short review of Community detection methods (on graphs), and their applications. A **community** is a subset of a network whose members are *highly connected*, but *loosely connected* to others outside their community. Different community detection methods *can return differing communities* these algorithms are **heuristic-based**. **Dynamic community detection** involves tracking the *evolution of community structure* over time.
https://gist.github.com/wolfram77/09e64d6ba3ef080db5558feb2d32fdc0
Communities can be of the following **types**:
- Disjoint
- Overlapping
- Hierarchical
- Local.
The following **static** community detection **methods** exist:
- Spectral-based
- Statistical inference
- Optimization
- Dynamics-based
The following **dynamic** community detection **methods** exist:
- Independent community detection and matching
- Dependent community detection (evolutionary)
- Simultaneous community detection on all snapshots
- Dynamic community detection on temporal networks
**Applications** of community detection include:
- Criminal identification
- Fraud detection
- Criminal activities detection
- Bot detection
- Dynamics of epidemic spreading (dynamic)
- Cancer/tumor detection
- Tissue/organ detection
- Evolution of influence (dynamic)
- Astroturfing
- Customer segmentation
- Recommendation systems
- Social network analysis (both)
- Network summarization
- Privary, group segmentation
- Link prediction (both)
- Community evolution prediction (dynamic, hot field)
<br>
<br>
## References
- [Application Areas of Community Detection: A Review : PAPER](https://ieeexplore.ieee.org/document/8625349)

Community Detection on the GPU : NOTES

This paper discusses a GPU implementation of the Louvain community detection algorithm. Louvain algorithm obtains hierachical communities as a dendrogram through modularity optimization. Given an undirected weighted graph, all vertices are first considered to be their own communities. In the first phase, each vertex greedily decides to move to the community of one of its neighbours which gives greatest increase in modularity. If moving to no neighbour's community leads to an increase in modularity, the vertex chooses to stay with its own community. This is done sequentially for all the vertices. If the total change in modularity is more than a certain threshold, this phase is repeated. Once this local moving phase is complete, all vertices have formed their first hierarchy of communities. The next phase is called the aggregation phase, where all the vertices belonging to a community are collapsed into a single super-vertex, such that edges between communities are represented as edges between respective super-vertices (edge weights are combined), and edges within each community are represented as self-loops in respective super-vertices (again, edge weights are combined). Together, the local moving and the aggregation phases constitute a stage. This super-vertex graph is then used as input fof the next stage. This process continues until the increase in modularity is below a certain threshold. As a result from each stage, we have a hierarchy of community memberships for each vertex as a dendrogram.
Approaches to perform the Louvain algorithm can be divided into coarse-grained and fine-grained. Coarse-grained approaches process a set of vertices in parallel, while fine-grained approaches process all vertices in parallel. A coarse-grained hybrid-GPU algorithm using multi GPUs has be implemented by Cheong et al. which grabbed my attention. In addition, their algorithm does not use hashing for the local moving phase, but instead sorts each neighbour list based on the community id of each vertex.
https://gist.github.com/wolfram77/7e72c9b8c18c18ab908ae76262099329

Survey for extra-child-process package : NOTES

Useful additions to inbuilt child_process module.
📦 Node.js, 📜 Files, 📰 Docs.
Please see attached PDF for literature survey.
https://gist.github.com/wolfram77/d936da570d7bf73f95d1513d4368573e

Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...

For the PhD forum an abstract submission is required by 10th May, and poster by 15th May. The event is on 30th May.
https://gist.github.com/wolfram77/1c1f730d20b51e0d2c6d477fd3713024

Fast Incremental Community Detection on Dynamic Graphs : NOTES

In this paper, the authors describe two approaches for dynamic community detection using the CNM algorithm. CNM is a hierarchical, agglomerative algorithm that greedily maximizes modularity. They define two approaches: BasicDyn and FastDyn. BasicDyn backtracks merges of communities until each marked (changed) vertex is its own singleton community. FastDyn undoes a merge only if the quality of merge, as measured by the induced change in modularity, has significantly decreased compared to when the merge initially took place. FastDyn also allows more than two vertices to contract together if in the previous time step these vertices eventually ended up contracted in the same community. In the static case, merging several vertices together in one contraction phase could lead to deteriorating results. FastDyn is able to do this, however, because it uses information from the merges of the previous time step. Intuitively, merges that previously occurred are more likely to be acceptable later.
https://gist.github.com/wolfram77/1856b108334cc822cdddfdfa7334792a

Can you ﬁx farming by going back 8000 years : NOTES

1. Human population didn't explode, but plateued.
2. Fertilizer prices are going to the sky.
3. Farmers are looking for alternatives such as animal waste (manure) or even human waste.
4. Manure prices are also going up.
5. Switching to organic farming not an option.
https://gist.github.com/wolfram77/49067fc3ddc1ba2e1db4f873056fd88a

About TrueTime, Spanner, Clock synchronization, CAP theorem, Two-phase lockin...

About TrueTime, Spanner, Clock synchronization, CAP theorem, Two-phase lockin...

Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

Adjusting Bitset for graph : SHORT REPORT / NOTES

Adjusting Bitset for graph : SHORT REPORT / NOTES

Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...

Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...

Adjusting primitives for graph : SHORT REPORT / NOTES

Adjusting primitives for graph : SHORT REPORT / NOTES

Experiments with Primitive operations : SHORT REPORT / NOTES

Experiments with Primitive operations : SHORT REPORT / NOTES

PageRank Experiments : SHORT REPORT / NOTES

PageRank Experiments : SHORT REPORT / NOTES

Algorithmic optimizations for Dynamic Monolithic PageRank (from STICD) : SHOR...

Algorithmic optimizations for Dynamic Monolithic PageRank (from STICD) : SHOR...

Adjusting OpenMP PageRank : SHORT REPORT / NOTES

Adjusting OpenMP PageRank : SHORT REPORT / NOTES

word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...

word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...

DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES

DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES

Shared memory Parallelism (NOTES)

Shared memory Parallelism (NOTES)

A Dynamic Algorithm for Local Community Detection in Graphs : NOTES

A Dynamic Algorithm for Local Community Detection in Graphs : NOTES

Scalable Static and Dynamic Community Detection Using Grappolo : NOTES

Scalable Static and Dynamic Community Detection Using Grappolo : NOTES

Application Areas of Community Detection: A Review : NOTES

Application Areas of Community Detection: A Review : NOTES

Community Detection on the GPU : NOTES

Community Detection on the GPU : NOTES

Survey for extra-child-process package : NOTES

Survey for extra-child-process package : NOTES

Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...

Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...

Fast Incremental Community Detection on Dynamic Graphs : NOTES

Fast Incremental Community Detection on Dynamic Graphs : NOTES

Can you ﬁx farming by going back 8000 years : NOTES

Can you ﬁx farming by going back 8000 years : NOTES

GBSN - Microbiology (Unit 2) Antimicrobial agents

Antimicrobial Agents in Therapy

2001_Book_HumanChromosomes - Genéticapdf

Livro sobre Cromossomos Humanos / Genética

Nutaceuticsls herbal drug technology CVS, cancer.pptx

Herbal drug technology views on nutaceuticsls

Anti-Universe And Emergent Gravity and the Dark Universe

Recent theoretical progress indicates that spacetime and gravity emerge together from the entanglement structure of an underlying microscopic theory. These ideas are best understood in Anti-de Sitter space, where they rely on the area law for entanglement entropy. The extension to de Sitter space requires taking into account the entropy and temperature associated with the cosmological horizon. Using insights from string theory, black hole physics and quantum information theory we argue that the positive dark energy leads to a thermal volume law contribution to the entropy that overtakes the area law precisely at the cosmological horizon. Due to the competition between area and volume law entanglement the microscopic de Sitter states do not thermalise at sub-Hubble scales: they exhibit memory effects in the form of an entropy displacement caused by matter. The emergent laws of gravity contain an additional ‘dark’ gravitational force describing the ‘elastic’ response due to the entropy displacement. We derive an estimate of the strength of this extra force in terms of the baryonic mass, Newton’s constant and the Hubble acceleration scale a0 = cH0, and provide evidence for the fact that this additional ‘dark gravity force’ explains the observed phenomena in galaxies and clusters currently attributed to dark matter.

Mechanisms and Applications of Antiviral Neutralizing Antibodies - Creative B...

Neutralizing antibodies, pivotal in immune defense, specifically bind and inhibit viral pathogens, thereby playing a crucial role in protecting against and mitigating infectious diseases. In this slide, we will introduce what antibodies and neutralizing antibodies are, the production and regulation of neutralizing antibodies, their mechanisms of action, classification and applications, as well as the challenges they face.

Mites,Slug,Snail_Infesting agricultural crops.pdf

Order : Trombidiformes (Acarina) Class : Arachnida
Mites normally feed on the undersurface of the leaves but the symptoms are more easily seen on the uppersurface.
Tetranychids produce blotching (Spots) on the leaf-surface.
Tarsonemids and Eriophyids produce distortion (twist), puckering (Folds) or stunting (Short) of leaves.
Eriophyids produce distinct galls or blisters (fluid-filled sac in the outer layer)

Farming systems analysis: what have we learnt?.pptx

Presentation given at the official farewell of Prof Ken Gillet at Wageningen on 13 June 2024

Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...

Context. The observation of several L-band emission sources in the S cluster has led to a rich discussion of their nature. However, a definitive answer to the classification of the dusty objects requires an explanation for the detection of compact Doppler-shifted Brγ emission. The ionized hydrogen in combination with the observation of mid-infrared L-band continuum emission suggests that most of these sources are embedded in a dusty envelope. These embedded sources are part of the S-cluster, and their relationship to the S-stars is still under debate. To date, the question of the origin of these two populations has been vague, although all explanations favor migration processes for the individual cluster members. Aims. This work revisits the S-cluster and its dusty members orbiting the supermassive black hole SgrA* on bound Keplerian orbits from a kinematic perspective. The aim is to explore the Keplerian parameters for patterns that might imply a nonrandom distribution of the sample. Additionally, various analytical aspects are considered to address the nature of the dusty sources. Methods. Based on the photometric analysis, we estimated the individual H−K and K−L colors for the source sample and compared the results to known cluster members. The classification revealed a noticeable contrast between the S-stars and the dusty sources. To fit the flux-density distribution, we utilized the radiative transfer code HYPERION and implemented a young stellar object Class I model. We obtained the position angle from the Keplerian fit results; additionally, we analyzed the distribution of the inclinations and the longitudes of the ascending node. Results. The colors of the dusty sources suggest a stellar nature consistent with the spectral energy distribution in the near and midinfrared domains. Furthermore, the evaporation timescales of dusty and gaseous clumps in the vicinity of SgrA* are much shorter ( 2yr) than the epochs covered by the observations (≈15yr). In addition to the strong evidence for the stellar classification of the D-sources, we also find a clear disk-like pattern following the arrangements of S-stars proposed in the literature. Furthermore, we find a global intrinsic inclination for all dusty sources of 60 ± 20◦, implying a common formation process. Conclusions. The pattern of the dusty sources manifested in the distribution of the position angles, inclinations, and longitudes of the ascending node strongly suggests two different scenarios: the main-sequence stars and the dusty stellar S-cluster sources share a common formation history or migrated with a similar formation channel in the vicinity of SgrA*. Alternatively, the gravitational influence of SgrA* in combination with a massive perturber, such as a putative intermediate mass black hole in the IRS 13 cluster, forces the dusty objects and S-stars to follow a particular orbital arrangement. Key words. stars: black holes– stars: formation– Galaxy: center– galaxies: star formation

Reaching the age of Adolescence- Class 8

This is a presentation on understanding the age of adolescence.

LEARNING TO LIVE WITH LAWS OF MOTION .pptx

CLASS 11 PHYSICS PPT

Post translation modification by Suyash Garg

overview of PTM helps to the students who wants to clear their basics about it.

Firoozeh Kashani-Sabet - An Esteemed Professor

Dr. Firoozeh Kashani-Sabet is an innovator in Middle Eastern Studies and approaches her work, particularly focused on Iran, with a depth and commitment that has resulted in multiple book publications. She is notable for her work with the University of Pennsylvania, where she serves as the Walter H. Annenberg Professor of History.

Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...

Wereport the study of a huge optical intraday flare on 2021 November 12 at 2 a.m. UT in the blazar OJ287. In the binary black hole model, it is associated with an impact of the secondary black hole on the accretion disk of the primary. Our multifrequency observing campaign was set up to search for such a signature of the impact based on a prediction made 8 yr earlier. The first I-band results of the flare have already been reported by Kishore et al. (2024). Here we combine these data with our monitoring in the R-band. There is a big change in the R–I spectral index by 1.0 ±0.1 between the normal background and the flare, suggesting a new component of radiation. The polarization variation during the rise of the flare suggests the same. The limits on the source size place it most reasonably in the jet of the secondary BH. We then ask why we have not seen this phenomenon before. We show that OJ287 was never before observed with sufficient sensitivity on the night when the flare should have happened according to the binary model. We also study the probability that this flare is just an oversized example of intraday variability using the Krakow data set of intense monitoring between 2015 and 2023. We find that the occurrence of a flare of this size and rapidity is unlikely. In machine-readable Tables 1 and 2, we give the full orbit-linked historical light curve of OJ287 as well as the dense monitoring sample of Krakow.

seed production, Nursery & Gardening.pdf

This presentation offers a general idea of the structure of seed, seed production, management of seeds and its allied technologies. It also offers the concept of gene erosion and the practices used to control it. Nursery and gardening have been widely explored along with their importance in the related domain.

Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...

A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!

Lattice Defects in ionic solid compound.pptx

lattice of ionic solid

WEB PROGRAMMING bharathiar university bca unitII

web programming unitII

BANANA BUNCHY TOP K R.pptx

BANANA BUNCHY TOP K R.pptx

GBSN - Microbiology (Unit 2) Antimicrobial agents

GBSN - Microbiology (Unit 2) Antimicrobial agents

2001_Book_HumanChromosomes - Genéticapdf

2001_Book_HumanChromosomes - Genéticapdf

Nutaceuticsls herbal drug technology CVS, cancer.pptx

Nutaceuticsls herbal drug technology CVS, cancer.pptx

Anti-Universe And Emergent Gravity and the Dark Universe

Anti-Universe And Emergent Gravity and the Dark Universe

Mechanisms and Applications of Antiviral Neutralizing Antibodies - Creative B...

Mechanisms and Applications of Antiviral Neutralizing Antibodies - Creative B...

Mites,Slug,Snail_Infesting agricultural crops.pdf

Mites,Slug,Snail_Infesting agricultural crops.pdf

Farming systems analysis: what have we learnt?.pptx

Farming systems analysis: what have we learnt?.pptx

Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...

Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...

Reaching the age of Adolescence- Class 8

Reaching the age of Adolescence- Class 8

LEARNING TO LIVE WITH LAWS OF MOTION .pptx

LEARNING TO LIVE WITH LAWS OF MOTION .pptx

Post translation modification by Suyash Garg

Post translation modification by Suyash Garg

The Powders And The Granules 123456.pptx

The Powders And The Granules 123456.pptx

Firoozeh Kashani-Sabet - An Esteemed Professor

Firoozeh Kashani-Sabet - An Esteemed Professor

Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...

Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...

seed production, Nursery & Gardening.pdf

seed production, Nursery & Gardening.pdf

Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...

Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...

Lattice Defects in ionic solid compound.pptx

Lattice Defects in ionic solid compound.pptx

WEB PROGRAMMING bharathiar university bca unitII

WEB PROGRAMMING bharathiar university bca unitII

BANANA BUNCHY TOP K R.pptx

BANANA BUNCHY TOP K R.pptx

Juaristi, Jon. - El canon espanol. El legado de la cultura española a la civi...

Juaristi, Jon. - El canon espanol. El legado de la cultura española a la civi...

- 1. Dynamic Batch Parallel Algorithms for Updating PageRank Subhajit Sahu†, Kishore Kothapalli† and Dip Sankar Banerjee‡ †International Institute of Information Technology Hyderabad, India. ‡Indian Institute of Technology Jodhpur, India. subhajit.sahu@research.,kkishore@iiit.ac.in, dipsankarb@iitj.ac.in This work is partially supported by a grant from the Department of Science and Technology (DST), India, under the National Supercomputing Mission (NSM) R&D in Exascale initiative vide Ref. No: DST/NSM/R&D Exascale/2021/16.
- 2. Facebook is taking a page out of Google’s playbook to stop fake news from going viral Published Apr 2019 by Salvador Rodriguez Click-Gap: When is Facebook is driving disproportionate amounts of traffic to websites. Effort to rid fakes news from Facebook’s services. Is a website relying on Facebook to drive significant traffic, but not well ranked by the rest of the web? Also News Citation Graph.
- 3. PAGERANK APPLICATIONS Ranking of websites. Measuring scientific impact of researchers. Finding the best teams and athletes. Ranking companies by talent concentration. Predicting road/foot traffic in urban spaces. Analysing protein networks. Finding the most authoritative news sources Identifying parts of brain that change jointly. Toxic waste management.
- 4. PAGERANK APPLICATIONS Debugging complex software systems(MonitorRank) Finding the most original writers (BookRank) Finding topical authorities (TwitterRank)
- 5. WHAT IS PAGERANK PageRank is a link-analysis algorithm. By Larry Page and Sergey Brin in 1996. For ordering information on the web. Represented with a random-surfer model. Rank of a page is defined recursively. Calculate iteratively with power-iteration.
- 6. PageRank computation approaches Matrix multiplication. Power-iteration (push vs pull). Random walk (approximate).
- 7. Challenges & Limitations Graphs are massive and constantly updated. Existing dynamic algorithms do not utilize reducibility of graphs. Vertices which are dependent upon other vertices to converge are still processed. Locality benefits of SCCs are not explored.
- 9. Types of Dynamic graph algorithms Incremental: handles 1 edge/vertex insertion. Decremental: handles 1 edge/vertex deletion. Fully dynamic: handles 1 insertion or deletion. Batched fully dynamic: handles n insertions and/or deletions.
- 10. Beneﬁts of Dynamic graph algorithms Reduces time needed for performing analytics. Enables interactivity with dataset. Batched fully dynamic algorithms accept a batch of updates to minimize computation needed in contrast to single-update fully dynamic ones.
- 11. Our Approaches: On graph update
- 12. Our Approaches: Computation procedure
- 13. Our Approaches: GPU-speciﬁc optimization
- 14. OUR APPROACHES: DynamicMonolithicPR Full power-iteration, process all vertices. Group vertices by SCC for better access. Partition vertices by in-degree on GPU. Use old ranks, skip unaffected vertices. Affected vertices found with DFS. Faster on GPU with CUDA.
- 16. OUR APPROACHES: DynamicLevelwisePR Contrast to full power-iteration. Process vertices in levels of SCCs. Avoid converged/unstable vertices. No per-iteration sharing of ranks. Faster on CPU with OpenMP. Slightly higher error. Requires graph to be dead-end free.
- 18. Dataset From the SuiteSparse Matrix Collection. Add self-loops to dead ends in all graphs. Number of vertices vary from 75k to 41M. Number of edges vary from 524k to 1.1B.
- 19. Batch generation Batch sizes vary from 500 to 10,000 edges. Edge insertions, deletions in equal mix. High degree vertices have higher chance of selection (mimic real-world graphs). No new vertices are added or removed.
- 20. Platform Intel(R) Xeon(R) Silver 4116 CPU (12 cores) x 2; Cache L1: 768KB, L2: 12MB, L3: 16MB (shared). NVIDIA Tesla V100 GPU (16GB PCIe); 14 TFLOPs SP (84 SMs x 64 FP/INT cores), 16GB 900GB/s HBM2 DRAM, 32 GB/s PCIe. CentOS 7.9, OpenMP 5.0, CUDA 11.3, GCC 9.3.
- 21. Performance measurement Damping factor d of 0.15. Tolerance τ of 10−6. Maximum of 500 iterations. 32-bit integers for CSR representation. 32-bit floats for rank vector. L∞-norm for error measurement, (L2-norm for nvGraph PageRank). Measured time only rank computation.
- 22. Results: Comparison with state-of-the-art CPU AM time for batches of 500, 1000, 2000, 5000, 10000 6.1×, 8.6× wrt static plain STIC-D PR [1]. 4.2×, 5.8× wrt Pure CPU HyPR [2].
- 23. Results: Comparison with state-of-the-art GPU AM time for batches of 500, 1000, 2000, 5000, 10000 9.8×, 9.3× wrt naive dynamic nvGraph PR. 1.9×, 1.8× wrt Pure GPU HyPR.
- 24. Results: Batched vs Cumulative update CPU time for batches of 500, 1000, 5000, 10000 4066×, 2998× of 5000 edges batch wrt single-edge cumulative update.
- 25. Results: Batched vs Cumulative update GPU time for batches of 500, 1000, 5000, 10000 1712×, 2324× of 5000 edges batch wrt cumulative single-edge update.
- 26. Conclusion DynamicLevelwisePR is a suitable approach on CPU. On a GPU, smaller levels should be combined and processed at a time. On 1 SCC graphs, both algorithms perform ~identically.