Like with Bio products, the world is developing to become a more nature-aware ecosystem. The green initiative defines two main goals: reduce energy consumption and use basic natural sources in electrical energy production.
This lecture focuses on energy consumption of working software and its development processes, where each development phase plays a significant role. Considering any software development process, the energy is being consumed while problem analysis, constructing and evaluating the code as well. Software or hardware tools have to be used to implement energy consumption monitoring for software run at the top of selected operating systems and for evaluation of the energy consumption. Usual usage scenarios are to monitor energy usage of selected software. We will also look at the possibility to use these tools to measure how green is the process that produced the programs.
In this RichReport slidecast, Dr. Nick New from Optalysys describes how the company's optical processing technology delivers accelerated performance for FFTs and Bioinformatics.
"Our prototype is on track to achieve game-changing improvements to process times over current methods whilst providing high levels of accuracy that are associated with the best software processes.”
Watch the video: https://wp.me/p3RLHQ-hn0
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Dynamic, distributed and open business forces enterprises to support various critical requirements, such as, timely reacting to changes, properly reusing business assets and smoothly collaborating with external partners. Existing approaches focus on mechanisms dealing with heterogeneity, but there is a lack of frameworks enabling legacy business processes performing collaboration in an open service environment. This paper proposes the L2L service framework featuring reactive IoT event messaging and coordinator-based collaborating between autonomous enterprises. Along with the emerging of coordinators, L2L empowers on-the-fly business process collaboration with dynamic changes. We present our experiments with a real-world scenario from the shipping industry of China.
Structuring Big Data results to create new information: Smart Data. These Smart Data can be used to advance knowledge and support decision-making processes.
A close cooperation between industry and science creates better conditions for cutting-edge research in Data Engineering/Smart Data.
In this deck from the HPC User Forum in Milwaukee, Tim Barr from Cray presents: Perspective on HPC-enabled AI.
"Cray’s unique history in supercomputing and analytics has given us front-line experience in pushing the limits of CPU and GPU integration, network scale, tuning for analytics, and optimizing for both model and data parallelization. Particularly important to machine learning is our holistic approach to parallelism and performance, which includes extremely scalable compute, storage and analytics."
Watch the video: https://wp.me/p3RLHQ-hpw
Learn more: http://cray.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Covers basics Artificial neural networks and motivation for deep learning and explains certain deep learning networks, including deep belief networks and autoencoders. It also details challenges of implementing a deep learning network at scale and explains how we have implemented a distributed deep learning network over Spark.
Like with Bio products, the world is developing to become a more nature-aware ecosystem. The green initiative defines two main goals: reduce energy consumption and use basic natural sources in electrical energy production.
This lecture focuses on energy consumption of working software and its development processes, where each development phase plays a significant role. Considering any software development process, the energy is being consumed while problem analysis, constructing and evaluating the code as well. Software or hardware tools have to be used to implement energy consumption monitoring for software run at the top of selected operating systems and for evaluation of the energy consumption. Usual usage scenarios are to monitor energy usage of selected software. We will also look at the possibility to use these tools to measure how green is the process that produced the programs.
In this RichReport slidecast, Dr. Nick New from Optalysys describes how the company's optical processing technology delivers accelerated performance for FFTs and Bioinformatics.
"Our prototype is on track to achieve game-changing improvements to process times over current methods whilst providing high levels of accuracy that are associated with the best software processes.”
Watch the video: https://wp.me/p3RLHQ-hn0
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Dynamic, distributed and open business forces enterprises to support various critical requirements, such as, timely reacting to changes, properly reusing business assets and smoothly collaborating with external partners. Existing approaches focus on mechanisms dealing with heterogeneity, but there is a lack of frameworks enabling legacy business processes performing collaboration in an open service environment. This paper proposes the L2L service framework featuring reactive IoT event messaging and coordinator-based collaborating between autonomous enterprises. Along with the emerging of coordinators, L2L empowers on-the-fly business process collaboration with dynamic changes. We present our experiments with a real-world scenario from the shipping industry of China.
Structuring Big Data results to create new information: Smart Data. These Smart Data can be used to advance knowledge and support decision-making processes.
A close cooperation between industry and science creates better conditions for cutting-edge research in Data Engineering/Smart Data.
In this deck from the HPC User Forum in Milwaukee, Tim Barr from Cray presents: Perspective on HPC-enabled AI.
"Cray’s unique history in supercomputing and analytics has given us front-line experience in pushing the limits of CPU and GPU integration, network scale, tuning for analytics, and optimizing for both model and data parallelization. Particularly important to machine learning is our holistic approach to parallelism and performance, which includes extremely scalable compute, storage and analytics."
Watch the video: https://wp.me/p3RLHQ-hpw
Learn more: http://cray.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Covers basics Artificial neural networks and motivation for deep learning and explains certain deep learning networks, including deep belief networks and autoencoders. It also details challenges of implementing a deep learning network at scale and explains how we have implemented a distributed deep learning network over Spark.
Hadoop World 2011: Hadoop and Graph Data Management: Challenges and Opportuni...Cloudera, Inc.
As Hadoop rapidly becomes the universal standard for scalable data analysis and processing, it becomes increasingly important to understand its strengths and weaknesses for particular application scenarios in order to avoid inefficiency pitfalls. For example, Hadoop has great potential to perform scalable graph analysis, but recent research from Yale University has shown that a conventional approach to graph analysis is 1300 times less efficient than a more advanced approach. This session will give an overview of the advanced approach and then discuss further changes that are needed in the core Hadoop framework to take performance to the next level.
Scalable and Efficient Algorithms for Analysis of Massive, Streaming GraphsJason Riedy
Graph-structured data in network security, social networks, finance, and other applications not only are massive but also under continual evolution. The changes often are scattered across the graph, permitting novel parallel and incremental analysis algorithms. We discuss analysis algorithms for streaming graph data to maintain both local and global metrics with low latency and high efficiency.
Graph Analysis Trends and Opportunities -- CMG Performance and Capacity 2014Jason Riedy
High-performance graph analysis is unlocking knowledge in problems like anomaly detection in computer security, community structure in social networks, and many other data integration areas. While graphs provide a convenient abstraction, real-world problems' sparsity and lack of locality challenge current systems. This talk will cover current trends ranging from massive scales to low-power, low-latency systems and summarize opportunities and directions for graphs and computing systems.
An introduction to the STINGER dynamic graph structure and analysis package. Shows the motivation for STINGER, what has been done with it, and how you can use it. More at http://cc.gatech.edu/stinger
High-performance graph analysis is unlocking knowledge in computer security, bioinformatics, social networks, and many other data integration areas. Graphs provide a convenient abstraction for many data problems beyond linear algebra. Some problems map directly to linear algebra. Others, like community detection, look eerily similar to sparse linear algebra techniques. And then there are algorithms that strongly resist attempts at making them look like linear algebra. This talk will cover recent results with an emphasis on streaming graph problems where the graph changes and results need updated with minimal latency. We’ll also touch on issues of sensitivity and reliability where graph analysis needs to learn from numerical analysis and linear algebra.
Clustering Methods and Community Detection with NetworkX. A slide deck for the NTU Complexity Science Winter School.
For the accompanying iPython Notebook, visit: http://github.com/eflegara/NetStruc
Social networks are not new, even though websites like Facebook and Twitter might make you want to believe they are; and trust me- I’m not talking about Myspace! Social networks are extremely interesting models for human behavior, whose study dates back to the early twentieth century. However, because of those websites, data scientists have access to much more data than the anthropologists who studied the networks of tribes!
Because networks take a relationship-centered view of the world, the data structures that we will analyze model real world behaviors and community. Through a suite of algorithms derived from mathematical Graph theory we are able to compute and predict behavior of individuals and communities through these types of analyses. Clearly this has a number of practical applications from recommendation to law enforcement to election prediction, and more.
Knowledge Discovery in Environmental Management Dr. Aparna Varde
This is a research presentation by Aparna Varde during a summer research visit at the Max Planck Institute for Informatics, Saarbruecken, Germany in August 2015 within the research group of Dr. Gerhard Weikum, The presentation focuses on various aspects of data mining and knowledge discovery pertinent to environmental science and management. It encompasses three main topics: (1) decision support for the greening of data centers; (2) predictive analysis in urban planning and simulation; and (3) common sense knowledge for domain-specific KBs. It includes a few brief highlights on web and text mining in article / collocation error detection as well as in terminology evolution. This presentation is based on her relevant work as per August 2015, serving as an invited talk during this research visit.
Scalable Graph Convolutional Network Based Link Prediction on a Distributed G...miyurud
Graph Convolutional Networks (GCN) have become a popular means of performing link prediction due to the high accuracy offered by them. However, scaling such link prediction into large graphs of billions of vertices and edges with rich types of attributes is a significant issue to be addressed due to the storage and computation limitations of the machines. In this paper we present a scalable link prediction approach which conducts GCN training and link prediction on top of a distributed graph database server called JasmineGraph. We partition graph data and persist them in multiple workers. We implement parallel graph node embedding generation using GraphSAGE algorithm in multiple workers. Our approach avoids facing performance bottlenecks in GCN training using an intelligent scheduling algorithm. We show our approach scales well with an increasing number of partitions (2,4,8, and 16) using four real world data sets called Twitter, Amazon, Reddit, and DBLP-V11. JasmineGraph was able to train a GCN from the largest dataset DBLP-V11 ($>$ 9.3GB) in 11 hours and 40 minutes time using 16 workers on a single server while the original GraphSAGE implementation could not process it at all. The original GraphSAGE implementation processed the second largest dataset Reddit in 238 minutes while JasmineGraph took only 100 minutes on the same hardware with 16 workers leading to 2.4 times improved performance.
Platform for Big Data Analytics and Visual Analytics: CSIRO use cases. Februa...Tomasz Bednarz
Presented at the ACEMS workshop at QUT in February 2015.
Credits: whole project team (names listed in the first slide).
Approved by CSIRO to be shared externally.
Implementation of p pic algorithm in map reduce to handle big dataeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Large amount of data are produced daily from various fields such as science, economics,
engineering and health. The main challenge of pervasive computing is to store and analyze large amount of
data.This has led to the need for usable and scalable data applications and storage clusters. In this article, we
examine the hadoop architecture developed to deal with these problems. The Hadoop architecture consists of
the Hadoop Distributed File System (HDFS) and Mapreduce programming model, which enables storage and
computation on a set of commodity computers. In this study, a Hadoop cluster consisting of four nodes was
created.Regarding the data size and cluster size, Pi and Grep MapReduce applications, which show the effect of
different data sizes and number of nodes in the cluster, have been made and their results examined.
Data-centric AI and the convergence of data and model engineering:opportunit...Paolo Missier
A keynote talk given to the IDEAL 2023 conference (Evora, Portugal Nov 23, 2023).
Abstract.
The past few years have seen the emergence of what the AI community calls "Data-centric AI", namely the recognition that some of the limiting factors in AI performance are in fact in the data used for training the models, as much as in the expressiveness and complexity of the models themselves. One analogy is that of a powerful engine that will only run as fast as the quality of the fuel allows. A plethora of recent literature has started the connection between data and models in depth, along with startups that offer "data engineering for AI" services. Some concepts are well-known to the data engineering community, including incremental data cleaning, multi-source integration, or data bias control; others are more specific to AI applications, for instance the realisation that some samples in the training space are "easier to learn from" than others. In this "position talk" I will suggest that, from an infrastructure perspective, there is an opportunity to efficiently support patterns of complex pipelines where data and model improvements are entangled in a series of iterations. I will focus in particular on end-to-end tracking of data and model versions, as a way to support MLDev and MLOps engineers as they navigate through a complex decision space.
Hadoop World 2011: Hadoop and Graph Data Management: Challenges and Opportuni...Cloudera, Inc.
As Hadoop rapidly becomes the universal standard for scalable data analysis and processing, it becomes increasingly important to understand its strengths and weaknesses for particular application scenarios in order to avoid inefficiency pitfalls. For example, Hadoop has great potential to perform scalable graph analysis, but recent research from Yale University has shown that a conventional approach to graph analysis is 1300 times less efficient than a more advanced approach. This session will give an overview of the advanced approach and then discuss further changes that are needed in the core Hadoop framework to take performance to the next level.
Scalable and Efficient Algorithms for Analysis of Massive, Streaming GraphsJason Riedy
Graph-structured data in network security, social networks, finance, and other applications not only are massive but also under continual evolution. The changes often are scattered across the graph, permitting novel parallel and incremental analysis algorithms. We discuss analysis algorithms for streaming graph data to maintain both local and global metrics with low latency and high efficiency.
Graph Analysis Trends and Opportunities -- CMG Performance and Capacity 2014Jason Riedy
High-performance graph analysis is unlocking knowledge in problems like anomaly detection in computer security, community structure in social networks, and many other data integration areas. While graphs provide a convenient abstraction, real-world problems' sparsity and lack of locality challenge current systems. This talk will cover current trends ranging from massive scales to low-power, low-latency systems and summarize opportunities and directions for graphs and computing systems.
An introduction to the STINGER dynamic graph structure and analysis package. Shows the motivation for STINGER, what has been done with it, and how you can use it. More at http://cc.gatech.edu/stinger
High-performance graph analysis is unlocking knowledge in computer security, bioinformatics, social networks, and many other data integration areas. Graphs provide a convenient abstraction for many data problems beyond linear algebra. Some problems map directly to linear algebra. Others, like community detection, look eerily similar to sparse linear algebra techniques. And then there are algorithms that strongly resist attempts at making them look like linear algebra. This talk will cover recent results with an emphasis on streaming graph problems where the graph changes and results need updated with minimal latency. We’ll also touch on issues of sensitivity and reliability where graph analysis needs to learn from numerical analysis and linear algebra.
Clustering Methods and Community Detection with NetworkX. A slide deck for the NTU Complexity Science Winter School.
For the accompanying iPython Notebook, visit: http://github.com/eflegara/NetStruc
Social networks are not new, even though websites like Facebook and Twitter might make you want to believe they are; and trust me- I’m not talking about Myspace! Social networks are extremely interesting models for human behavior, whose study dates back to the early twentieth century. However, because of those websites, data scientists have access to much more data than the anthropologists who studied the networks of tribes!
Because networks take a relationship-centered view of the world, the data structures that we will analyze model real world behaviors and community. Through a suite of algorithms derived from mathematical Graph theory we are able to compute and predict behavior of individuals and communities through these types of analyses. Clearly this has a number of practical applications from recommendation to law enforcement to election prediction, and more.
Knowledge Discovery in Environmental Management Dr. Aparna Varde
This is a research presentation by Aparna Varde during a summer research visit at the Max Planck Institute for Informatics, Saarbruecken, Germany in August 2015 within the research group of Dr. Gerhard Weikum, The presentation focuses on various aspects of data mining and knowledge discovery pertinent to environmental science and management. It encompasses three main topics: (1) decision support for the greening of data centers; (2) predictive analysis in urban planning and simulation; and (3) common sense knowledge for domain-specific KBs. It includes a few brief highlights on web and text mining in article / collocation error detection as well as in terminology evolution. This presentation is based on her relevant work as per August 2015, serving as an invited talk during this research visit.
Scalable Graph Convolutional Network Based Link Prediction on a Distributed G...miyurud
Graph Convolutional Networks (GCN) have become a popular means of performing link prediction due to the high accuracy offered by them. However, scaling such link prediction into large graphs of billions of vertices and edges with rich types of attributes is a significant issue to be addressed due to the storage and computation limitations of the machines. In this paper we present a scalable link prediction approach which conducts GCN training and link prediction on top of a distributed graph database server called JasmineGraph. We partition graph data and persist them in multiple workers. We implement parallel graph node embedding generation using GraphSAGE algorithm in multiple workers. Our approach avoids facing performance bottlenecks in GCN training using an intelligent scheduling algorithm. We show our approach scales well with an increasing number of partitions (2,4,8, and 16) using four real world data sets called Twitter, Amazon, Reddit, and DBLP-V11. JasmineGraph was able to train a GCN from the largest dataset DBLP-V11 ($>$ 9.3GB) in 11 hours and 40 minutes time using 16 workers on a single server while the original GraphSAGE implementation could not process it at all. The original GraphSAGE implementation processed the second largest dataset Reddit in 238 minutes while JasmineGraph took only 100 minutes on the same hardware with 16 workers leading to 2.4 times improved performance.
Platform for Big Data Analytics and Visual Analytics: CSIRO use cases. Februa...Tomasz Bednarz
Presented at the ACEMS workshop at QUT in February 2015.
Credits: whole project team (names listed in the first slide).
Approved by CSIRO to be shared externally.
Implementation of p pic algorithm in map reduce to handle big dataeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Large amount of data are produced daily from various fields such as science, economics,
engineering and health. The main challenge of pervasive computing is to store and analyze large amount of
data.This has led to the need for usable and scalable data applications and storage clusters. In this article, we
examine the hadoop architecture developed to deal with these problems. The Hadoop architecture consists of
the Hadoop Distributed File System (HDFS) and Mapreduce programming model, which enables storage and
computation on a set of commodity computers. In this study, a Hadoop cluster consisting of four nodes was
created.Regarding the data size and cluster size, Pi and Grep MapReduce applications, which show the effect of
different data sizes and number of nodes in the cluster, have been made and their results examined.
Data-centric AI and the convergence of data and model engineering:opportunit...Paolo Missier
A keynote talk given to the IDEAL 2023 conference (Evora, Portugal Nov 23, 2023).
Abstract.
The past few years have seen the emergence of what the AI community calls "Data-centric AI", namely the recognition that some of the limiting factors in AI performance are in fact in the data used for training the models, as much as in the expressiveness and complexity of the models themselves. One analogy is that of a powerful engine that will only run as fast as the quality of the fuel allows. A plethora of recent literature has started the connection between data and models in depth, along with startups that offer "data engineering for AI" services. Some concepts are well-known to the data engineering community, including incremental data cleaning, multi-source integration, or data bias control; others are more specific to AI applications, for instance the realisation that some samples in the training space are "easier to learn from" than others. In this "position talk" I will suggest that, from an infrastructure perspective, there is an opportunity to efficiently support patterns of complex pipelines where data and model improvements are entangled in a series of iterations. I will focus in particular on end-to-end tracking of data and model versions, as a way to support MLDev and MLOps engineers as they navigate through a complex decision space.
Current Trends and Challenges in Big Data BenchmarkingeXascale Infolab
Years ago, it was common to write a for-loop and call it benchmark. Nowadays, benchmarks are complex pieces of software and specifications. In this talk, the idea of benchmark engineering, trends in the area of benchmarking research and current efforts of the SPEC Research Group and the WBDB community focusing on Big Data will be discussed. The way in which benchmarks are used has changed. Traditionally, they were mostly used for generating throughput numbers. Today, benchmarks are, e.g., used as test frameworks to evaluate different aspects of systems such as scalability or performance. Since benchmarks provide standardized workloads and meaningful metrics, they are increasingly important for research.
The benchmark community is currently focusing on new trends such as cloud computing, big data, power-consumption and large scale, highly distributed systems. For several of these trends traditional benchmarking approaches fail: how can we benchmark a highly distributed system with thousands of nodes and data sources? What does a typical Big Data workload look like and how does it scale? How can we benchmark a real world setup in a realistic way on limited resources? What does performance mean in the context of Big Data? What is the right metric?
Speaker: Kai Sachs is a member of the Lifecycle & Cloud Management group at SAP AG. He received a joint Diploma degree in business administration and computer science as well as a PhD degree from Technische Universität Darmstadt. His PhD thesis was awarded with the SPEC Distinguished Dissertation Award 2011 for outstanding contributions in the area of performance evaluation and benchmarking. His research interests include software performance engineering, capacity planning, cloud computing and benchmarking. He is co-founder of ACM/SPEC International Conference on Performance Engineering (ICPE). He has served as member of several program and organization committees and as reviewer for many conferences and journals. Among others he was the PC Chair of the SPEC Benchmark Workshop 2010, Program Chair of the Workshop on Hot Topics on Cloud Services 2013 and the Industrial PC Chair of the ICPE 2011. Kai Sachs is currently serving on the editorial board of the CSI Transactions on ICT, as vice-chair of the SPEC Research Group, as PC Co-Chair of the ACM/SPEC ICPE 2015 and as Co-Chair of the Workshop on Big Data Benchmarking 2014.
Big Data and Big Data Management (BDM) with current Technologies –ReviewIJERA Editor
The emerging phenomenon called ―Big Data‖ is pushing numerous changes in businesses and several other organizations, Domains, Fields, areas etc. Many of them are struggling just to manage the massive data sets. Big data management is about two things - ―Big data‖ and ―Data Management‖ and these terms work together to achieve business and technology goals as well. In previous few years data generation have tremendously enhanced due to digitization of data. Day by day new computer tools and technologies for transmission of data among several computers through Internet is been increasing. It‗s relevance and importance in the context of applicability, usefulness for decision making, performance improvement etc in all areas have emerged very fast to be relevant in today‗s era. Big data management also has numerous challenges and common complexities include low organizational maturity relative to big data, weak business support, and the need to learn new technology approaches. This paper will discuss the impacts of Big Data and issues related to data management using current technologies
Programming Modes and Performance of Raspberry-Pi ClustersAM Publications
In present times, updated information and knowledge has become readily accessible to researchers, enthusiasts, developers, and academics through the Internet on many different subjects for wider areas of application. The underlying framework facilitating such possibilities is networking of servers, nodes, and personal computers. However, such setups, comprising of mainframes, servers and networking devices are inaccessible to many, costly, and are not portable. In addition, students and lab-level enthusiasts do not have the requisite access to modify the functionality to suit specific purposes. The Raspberry-Pi (R-Pi) is a small device capable of many functionalities akin to super-computing while being portable, economical and flexible. It runs on open source Linux, making it a preferred choice for lab-level research and studies. Users have started using the embedded networking capability to design portable clusters that replace the costlier machines. This paper introduces new users to the most commonly used frameworks and some recent developments that best exploit the capabilities of R-Pi when used in clusters. This paper also introduces some of the tools and measures that rate efficiencies of clusters to help users assess the quality of cluster design. The paper aims to make users aware of the various parameters in a cluster environment.
This tutorial was held at IEEE BigData '14 on October 29, 2014 in Bethesda, ML, USA.
Presenters: Chaitan Baru and Tilmann Rabl
More information available at:
http://msrg.org/papers/BigData14-Rabl
Summary:
This tutorial will introduce the audience to the broad set of issues involved in defining big data benchmarks, for creating auditable industry-standard benchmarks that consider performance as well as price/performance. Big data benchmarks must capture the essential characteristics of big data applications and systems, including heterogeneous data, e.g. structured, semi- structured, unstructured, graphs, and streams; large-scale and evolving system configurations; varying system loads; processing pipelines that progressively transform data; workloads that include queries as well as data mining and machine learning operations and algorithms. Different benchmarking approaches will be introduced, from micro-benchmarks to application- level benchmarking.
Since May 2012, five workshops have been held on Big Data Benchmarking including participation from industry and academia. One of the outcomes of these meetings has been the creation of industry’s first big data benchmark, viz., TPCx-HS, the Transaction Processing Performance Council’s benchmark for Hadoop Systems. During these workshops, a number of other proposals have been put forward for more comprehensive big data benchmarking. The tutorial will present and discuss salient points and essential features of such benchmarks that have been identified in these meetings, by experts in big data as well as benchmarking. Two key approaches are now being pursued—one, called BigBench, is based on extending the TPC- Decision Support (TPC-DS) benchmark with big data applications characteristics. The other called Deep Analytics Pipeline, is based on modeling processing that is routinely encountered in real-life big data applications. Both will be discussed.
We conclude with a discussion of a number of future directions for big data benchmarking
Reproducible Linear Algebra from Application to ArchitectureJason Riedy
All computing must be parallel to take advantage of modern systems like multicore processors, GPUs, and distributed systems. Results that are not bit-wise reproducible introduce doubt on many levels. Sometimes that is appropriate. Reproducibility limitations occur because underlying libraries do not specify their reproducibility requirements. New advances in interfaces, algorithms, and architectures allow selecting among those requirements in the future. This talk covers many of the upcoming options and their trade-offs.
PEARC19: Wrangling Rogues: A Case Study on Managing Experimental Post-Moore A...Jason Riedy
The Rogues Gallery is a new experimental testbed that is focused on tackling "rogue'' architectures for the Post-Moore era of computing. While some of these devices have roots in the embedded and high-performance computing spaces, managing current and emerging technologies provides a challenge for system administration that are not always foreseen in traditional data center environments.
We present an overview of the motivations and design of the initial Rogues Gallery testbed and cover some of the unique challenges that we have seen and foresee with upcoming hardware prototypes for future post-Moore research. Specifically, we cover the networking, identity management, scheduling of resources, and tools and sensor access aspects of the Rogues Gallery and techniques we have developed to manage these new platforms. We argue that current tools like the Slurm resource manager can support new rogues without major infrastructure changes.
ICIAM 2019: Reproducible Linear Algebra from Application to ArchitectureJason Riedy
All computing must be parallel to take advantage of modern systems like multicore processors, GPUs, and distributed systems. Results that are not bit-wise reproducible introduce doubt on many levels. Sometimes that is appropriate. Reproducibility limitations occur because underlying libraries do not specify their reproducibility requirements. New advances in interfaces, algorithms, and architectures allow selecting among those requirements in the future. This talk covers many of the upcoming options and their trade-offs.
ICIAM 2019: A New Algorithm Model for Massive-Scale Streaming Graph AnalysisJason Riedy
Applications in many areas analyze an ever-changing environment. On billion vertices graphs, providing snapshots imposes a large performance cost. We propose the first formal model for graph analysis running concurrently with streaming data updates. We consider an algorithm valid if its output is correct for the initial graph plus some implicit subset of concurrent changes. We show theoretical properties of the model, demonstrate the model on various algorithms, and extend it to updating results incrementally.
In one classic sense a rogue is someone who goes their own way, who breaks away from the crowd. The CRNCH Rogues Gallery aims to support computer architecture rogues by being a physical and virtual space providing access to novel computing architectures. Researchers find applications, and architects discover what happens when their prototypes hit reality. Our goals are to help kick-start software ecosystems, train students in novel system evaluation and use, and provide rapid feedback to architects. By exposing students and researchers to this set of unique hardware, we foster cross-cutting discussions about hardware designs that will drive future performance improvements in computing long after the Moore’s Law era of “cheap transistors” ends. We provide a brief description of the current Rogues Gallery along with successes and research highlights over the last year.
Augmented Arithmetic Operations Proposed for IEEE-754 2018Jason Riedy
Algorithms for extending arithmetic precision through compensated summation or arithmetics like double-double rely on operations commonly called twoSum and twoProduct. The current draft of the IEEE 754 standard specifies these operations under the names augmentedAddition and augmentedMultiplication. These operations were included after three decades of experience because of a motivating new use: bitwise reproducible arithmetic. Standardizing the operations provides a hardware acceleration target that can provide at least a 33% speed improvements in reproducible dot product, placing reproducible dot product almost within a factor of two of common dot product. This paper provides history and motivation for standardizing these operations. We also define the operations, explain the rationale for all the specific choices, and provide parameterized test cases for new boundary behaviors.
CRNCH Rogues Gallery: A Community Core for Novel Computing PlatformsJason Riedy
The Rogues Gallery is a new concept focused on developing our understanding of next-generation hardware with a focus on unorthodox and uncommon technologies. This project, initiated by Georgia Tech's Center for Research into Novel Computing Hierarchies (CRNCH), will acquire new and unique hardware (ie, the aforementioned "rogues") from vendors, research labs, and startups and make this hardware available to students, faculty, and industry collaborators within a managed data center environment. By exposing students and researchers to this set of unique hardware, we hope to foster cross-cutting discussions about hardware designs that will drive future performance improvements in computing long after the Moore's Law era of "cheap transistors" ends.
A New Algorithm Model for Massive-Scale Streaming Graph AnalysisJason Riedy
Applications in computer network security, social media analysis,and other areas rely on analyzing a changing environment. The data is rich in relationships and lends itself to graph analysis. Traditional static graph analysis cannot keep pace with network security applications analyzing nearly one million events per second and social networks like Facebook collecting 500 thousand comments per second. Streaming frameworks like STINGER support ingesting up three million of edge changes per second but there are few streaming analysis kernels that keep up with these rates. Here we present a new algorithm model for applying complex metrics to a changing graph. In this model, many more algorithms can be applied without having to stop the world.
High-Performance Analysis of Streaming Graphs Jason Riedy
Graph-structured data in social networks, finance, network security, and others not only are massive but also under continual change. These changes often are scattered across the graph. Stopping the world to run a single, static query is infeasible. Repeating complex global analyses on massive snapshots to capture only what has changed is inefficient. We discuss requirements for single-shot queries on changing graphs as well as recent high-performance algorithms that update rather than recompute results. These algorithms are incorporated into our software framework for streaming graph analysis, STINGER.
High-Performance Analysis of Streaming GraphsJason Riedy
Graph-structured data in social networks, finance, network security, and others not only are massive but also under continual change. These changes often are scattered across the graph. Stopping the world to run a single, static query is infeasible. Repeating complex global analyses on massive snapshots to capture only what has changed is inefficient. We discuss requirements for single-shot queries on changing graphs as well as recent high-performance algorithms that update rather than recompute results. These algorithms are incorporated into our software framework for streaming graph analysis, STING (Spatio-Temporal Interaction Networks and Graphs).
Algorithm for efficiently and accurately updating PageRank as the graph changes from a stream of updates. Also includes needs from the upcoming GraphBLAS to support high-performance streaming graph analysis.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
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STING: Spatio-Temporal Interaction Networks and Graphs for Intel Platforms
1. STING: Spatio-Temporal Interaction
Networks and Graphs for Intel
Platforms
David Bader, Jason Riedy, David Ediger, Rob McColl,
Timothy G. Mattson∗, ...
Georgia Institute of Technology 17 January 2014
2. (insert current prefix here)scale data analysis
Health care Finding outbreaks, population epidemiology
Social networks Advertising, searching, grouping
Intelligence Decisions at scale, regulating algorithms
Systems biology Understanding interactions, drug design
Power grid Disruptions, conservation
Simulation Discrete events, cracking meshes
Data analysis runs throughout.
Many problems can be phrased through graphs.
Any many are changing and dynamic.
Bader, Riedy— STING 17 Jan. 2014 2 / 47
3. GT’s part of Intel’s Non-Numeric Computing Program
Goal
Supporting analysis of massive graphs under rapid change
across the spectrum of Intel-based platforms.
STING on Intel-based platforms
Maintain a graph and metrics under large data changes
Performance, documentation, distribution
Available at http://www.cc.gatech.edu/stinger/
Bader, Riedy— STING 17 Jan. 2014 3 / 47
4. Outline
Motivation: Graph Algorithms for Analysis
STING
Review of Accomplishments
Selected Details on Recent Work
Dynamic Community Updating
Incremental PageRank
Related Projects (Future Plans)
Artifacts of the Intel Non-Numeric Computing Program
Bader, Riedy— STING 17 Jan. 2014 4 / 47
5. Why Graphs?
Smaller than raw data.
Taught (roughly) to all CS
students...
Semantic attributions can capture
essential relationships.
Traversals can be faster than
filtering DB joins.
Provide clear phrasing for queries
about relationships.
Often next step after dense and sparse linear algebra.
Note: Extracting data into a graph is a separate topic, see
Intel’s GraphBuilder.
Bader, Riedy— STING 17 Jan. 2014 5 / 47
6. Graphs: A Fundamental Abstraction
Structure for “unstructured” data
Traditional uses:
Route planning on fixed routes
Logistic planning between sources, routes, destinations
Increasing uses:
Computer security: Identify anomalies (e.g. spam, viruses,
hacks) as they occur, insider threats, control access,
localize malware
Data / intelligence integration: Find smaller, relevant
subsets of massive, “unstructured” data piles
Recommender systems (industry): Given activities,
automatically find other interesting data.
Bader, Riedy— STING 17 Jan. 2014 6 / 47
7. On to STING...
Motivation: Graph Algorithms for Analysis
STING
Review of Accomplishments
Selected Details on Recent Work
Related Projects (Future Plans)
Artifacts of the Intel Non-Numeric Computing Program
Bader, Riedy— STING 17 Jan. 2014 7 / 47
8. STING’s focus
Source data
predictionaction
summary
Control
VizSimulation / query
STING manages queries against changing graph data.
Visualization and control often are application specific.
Ideal: Maintain many persistent graph analysis kernels.
One current graph snapshot, kernels keep smaller histories.
Also (a harder goal), coordinate the kernels’ cooperation.
Bader, Riedy— STING 17 Jan. 2014 8 / 47
9. STING: High-level architecture
Slide credit: Rob McColl and David Ediger
OpenMP + sufficiently POSIX-ish
Multiple processes for resilience
Bader, Riedy— STING 17 Jan. 2014 9 / 47
10. STING Extensible Representation: Core data structure
source
vertex
edge
type
adj. vtx weight mod. time first time
edge type index
vertex
index
Initial considerations [Bader, et al.]
Be useful for the entire “large graph” community
Portable semantics and high-level optimizations across multiple platforms & frameworks (XMT C, MTGL,
etc.)
Permit good performance: No single structure is optimal for all.
Assume globally addressable memory access and atomic operations
Support multiple, parallel readers and a single writer process
Large graph ⇒ rare conflicts, may ignore synchronization
Bader, Riedy— STING 17 Jan. 2014 10 / 47
11. STING in use
Who is using it?
Internally, well, us. Some details later.
Outside institutions in multiple research projects
Center for Adaptive Supercomputing Software –
Multithreaded Architectures (CASS-MT) directed by PNNL
PRODIGAL team for DARPA ADAMS (Anomaly Detection at
Multiple Scales) including GTRI, SAIC, Oregon State U., U.
Mass., and CMU
Several industrial companies in the area of data analytics
and security
Well-attended tutorials given at PPoPP, in MD, and locally
Bader, Riedy— STING 17 Jan. 2014 11 / 47
12. STING: Where do you get it?
www.cc.gatech.edu/stinger/
Gateway to
code,
development,
documentation,
presentations...
(working on usage and
development screencasts)
Remember: Still academic code, but
maturing.
Bader, Riedy— STING 17 Jan. 2014 12 / 47
13. Motivation: Graph Algorithms for Analysis
STING
Review of Accomplishments
Selected Details on Recent Work
Related Projects (Future Plans)
Artifacts of the Intel Non-Numeric Computing Program
Bader, Riedy— STING 17 Jan. 2014 13 / 47
14. Selected Program Accomplishments I
In the beginning: Initial streaming algorithms
Maintaining clustering coefficients[1] (counting triangles)
Up to 130 000 graph updates per second on X5570
(Nehalem-EP, 2.93GHz)
2000× speed-up over static recomputation
Connected components[2]
Up to 88 700 graph updates per second on X5570, 233×
speed-up
Community detection[3, 4, 5]
First parallel algorithm for agglomerative community
detection, 3.3B edges clustered in 505s on a 4-socket,
10-core Westmere-EX.
Win 10th DIMACS Implementation Challenge’s Mix Challenge
Enabled moving computation from special server + batch to
laptops and servers.
Bader, Riedy— STING 17 Jan. 2014 14 / 47
15. Selected Program Accomplishments II
Still maintained, used by external groups like Cray, people
finally threatening performance.
Expanded streaming algorithms
Improved connected components[6]
Does not fall back on re-computation for deletions
Up to 137× speed-up over static re-computation
Community maintenance[7] and monitoring∗
Up to 100 million updates per second, speed-ups from 4× to
3 700×
Demonstrated at Research@Intel and GraphLab Workshop
2013
PageRank∗
Reduce edge traversals from 30% – 90%.
Betweenness centrality∗ [8, 9]
Up to 148× speed-up
Optimization and software structure of STING[10]
Bader, Riedy— STING 17 Jan. 2014 15 / 47
16. Selected Program Accomplishments III
Up to 14× performance improvement on Intel-based
platforms[11], best paper award at HPEC12
Research@Intel 2013 demo collaborative with Intel
GraphBuilder: extract and transform data from a Hadoop
store, transfer to STING for analysis
Something different:
10th DIMACS Implementation Challenge on Graph
Partitioning and Graph Clustering [12]
Very useful workshop, 13-14 February 2012 at Georgia Tech
Intel sponsorship: Thank you!
Graph archive being used in other projects (although not
always cited appropriately)
PASQUAL [13]
First de novo genome assembler that is both scalable and
reliable (doesn’t crash).
http://www.cc.gatech.edu/pasqual/ (not STING-based)
Bader, Riedy— STING 17 Jan. 2014 16 / 47
17. Motivation: Graph Algorithms for Analysis
STING
Review of Accomplishments
Selected Details on Recent Work
Dynamic Community Updating
Incremental PageRank
Related Projects (Future Plans)
Artifacts of the Intel Non-Numeric Computing Program
Bader, Riedy— STING 17 Jan. 2014 17 / 47
18. Community Detection
What do we mean?
Partition a graph’s
vertices into disjoint
communities.
Locally optimize some
metric, e.g. modularity,
conductance, ...
Try to capture that
vertices are more
similar within one
community than
between communities.
Jason’s network via LinkedIn Labs
Bader, Riedy— STING 17 Jan. 2014 18 / 47
19. Parallel agglomerative method
A
B
C
D
E
F
G
Use a matching to avoid a serializing
queue.
Compute a heavy weight, large
matching.
Simple greedy algorithm.
Maximal matching, within 2× max
weight.
Merge all matched communities in
parallel.
Highly scalable, 5.8 × 106 – 7 × 106
edges/sec, 8× – 16× speed-up on
80-thread E7-8870
Agnostic to weighting, matching...
Works with many weighting and
termination criteria.
Bader, Riedy— STING 17 Jan. 2014 19 / 47
20. Parallel agglomerative method
A
B
C
D
E
F
G
C
D
G
Use a matching to avoid a serializing
queue.
Compute a heavy weight, large
matching.
Simple greedy algorithm.
Maximal matching, within 2× max
weight.
Merge all matched communities in
parallel.
Highly scalable, 5.8 × 106 – 7 × 106
edges/sec, 8× – 16× speed-up on
80-thread E7-8870
Agnostic to weighting, matching...
Works with many weighting and
termination criteria.
Bader, Riedy— STING 17 Jan. 2014 19 / 47
21. Parallel agglomerative method
A
B
C
D
E
F
G
C
D
G
E
B
C
Use a matching to avoid a serializing
queue.
Compute a heavy weight, large
matching.
Simple greedy algorithm.
Maximal matching, within 2× max
weight.
Merge all matched communities in
parallel.
Highly scalable, 5.8 × 106 – 7 × 106
edges/sec, 8× – 16× speed-up on
80-thread E7-8870
Agnostic to weighting, matching...
Works with many weighting and
termination criteria.
Bader, Riedy— STING 17 Jan. 2014 19 / 47
22. Community Monitoring Algorithm
Given a batch of edge insertions and removals:
1. Collect the set of possibly moved vertices ∆V.
Add the endpoints∗ of every edge insertion and removal to
∆V.
(STING support routine, scatter/gather and atomic CAS.)
2. Extract each v ∈ ∆V from its community into a singleton.
This is the complex piece, see Riedy & Bader, MTAAP13 [7].
3. Re-start agglomeration from the expanded community
graph.
∗: More recent experiments (by Anita Zakrzewska) add larger
neighborhoods. Little effect on modularity, large effect on performance.
Bader, Riedy— STING 17 Jan. 2014 20 / 47
23. Platform: Intel® E7-4820-based server
Tolerates some latency by hyperthreading:
“Westmere-EX:” 2 threads / core, 8 cores / socket, four
sockets.
Fast cores (2.0 GHz), fast memory (1 066 MHz).
Not so many outstanding memory requests (60/socket), but
large caches (18 MiB L3 per socket).
Good system support
Transparent hugepages reduces TLB costs.
Fast, user-level locking. (HLE would be better...)
OpenMP, although I didn’t tune it...
Four processors (64
threads), 1 TiB memory
gcc 4.7.2, Linux kernel 3.9
pre-release
Acknowledgment: Donated by Oracle. Image: Intel® press kit
Bader, Riedy— STING 17 Jan. 2014 21 / 47
24. Platform: Cray/YarcData XMT2
Tolerates latency by massive multithreading:
Hardware: 128 threads per processor
Context switch on every cycle (500 MHz)
Many outstanding memory requests (180/proc)
“No” caches...
Flexibly supports dynamic load balancing
Globally hashed address space, no data cache
Support for fine-grained, word-level synchronization
Full/empty bit on with every memory word
64 processor XMT2 at
CSCS, the Swiss National
Supercomputer Centre.
500 MHz processors, 8192
threads, 2 TiB of shared
memory Image: cray.com
Bader, Riedy— STING 17 Jan. 2014 22 / 47
25. Test Data
Combination of real and artificial
Start with (small) real-world graphs from 10th DIMACS
Challenge collection:
Name |V| |E| |C| |EC|
caidaRouterLevel 192 244 1 218 132 18 343 30 776
coPapersDBLP 540 486 30 866 466 1 401 205 856
eu-2005 862 664 16 138 468 55 624 194 971
Generate artificial edge actions:
Compute an initial clustering.
Generate random edge insertions between and removals
within communities.
15/16: Insertions. Select communities ∝ volume, vertices ∝
1/degree.
1/16: Removals. Uniform sampling (w/o replacement) of
existing edges, then inserted edges.
Bader, Riedy— STING 17 Jan. 2014 23 / 47
26. Peak updates per second
There is speed-up per # of threads, etc. on large batches,
large graphs. Not the interesting story.
Graph Platform #T Batch Upd/Sec Speed-up∗ Latency(s)
caidaRouterLevel IA32-64 56 1e5 1.2e+7 4.0 8.3e-3
XMT2 56 1e6 2.5e+6 4.3 4.0e-1
coPapersDBLP IA32-64 20 1e6 2.9e+6 11 3.5e-1
XMT2 48 3e5 2.2e+6 21 1.4e-1
eu-2005 IA32-64 40 1e5 4.8e+6 330 2.1e-2
XMT2 64 1e6 2.1e+6 43 4.9e-1
Large batches,
many threads
⇒
> 106 updates per second, but
high latency
∗: Speed up over static recomputation. Quality doesn’t
decrease significantly v. static recomputation.
Bader, Riedy— STING 17 Jan. 2014 24 / 47
27. Least latency between updates
Graph Platform #T Batch Upd./Sec Speed-up∗ Latency (s)
caidaRouterLevel IA32-64 2 1 4.6e+2 430 2.2e-3
XMT2 4 30 5.2e+3 230 5.8e-3
coPapersDBLP IA32-64 4 30 7.0e+3 1900 4.3e-3
XMT2 40 1 1.4e+2 380 7.1e-3
eu-2005 IA32-64 12 1 4.2e+2 3500 2.4e-3
XMT2 20 10 1.5e+3 3200 6.5e-3
Small batches,
fewer threads
⇒
fast response, but < 104
updates per second
∗: Speed up over static recomputation. Quality doesn’t
decrease significantly v. static recomputation.
Bader, Riedy— STING 17 Jan. 2014 25 / 47
28. Incremental PageRank
PageRank: Well-understood method for ranking vertices
based on random walks (related to minimizing
conductance).
Equivalent problem, solve (I − αATD−1)x = (1 − α)v given
initial weights v.
Goal: Use for seed set expansion, sparse v.
State-of-the-art for updating x when the graph represented
by A changes? Re-start iteration with the previous x.
Can do significantly better for low-latency needs.
Compute the change ∆x instead of the entire new x.
Bader, Riedy— STING 17 Jan. 2014 26 / 47
29. Incremental PageRank: Iteration
Iterative solver
Step k → k + 1:
∆x(k+1)
= α(A + ∆A)T
(D + ∆D)−1
∆x(k)
+
α (A + ∆A)T
(D + ∆D)−1
− AT
D−1
x
Additive part: Non-zero only at changes.
Operator: Pushes changes outward.
Bader, Riedy— STING 17 Jan. 2014 27 / 47
30. Incremental PageRank: Limiting Expansion
Iterative solver
Step k → k + 1:
∆ˆx(k+1)
= α(A + ∆A)T
(D + ∆D)−1
∆ˆx
(k)
|ex + α∆ˆx|held
α (A + ∆A)T
(D + ∆D)−1
− AT
D−1 ˆx
Additive part: Non-zero only at changes.
Operator: Pushes sufficiently large changes outward.
Bader, Riedy— STING 17 Jan. 2014 28 / 47
31. Incremental PageRank: Test Cases
Initial, high-level implementation via sparse matrices in Julia.
Test graphs from the 10th DIMACS Implementation Challenge.
Add uniformly random edges... worst case.
Up to 100k in different batch sizes.
One sequence of edge actions per graph shared across
experiments.
Conv. & hold base threshold: 10−12
Graph |V| |E| Avg. Deg. Size (MiB)
caidaRouterLevel 192 244 609 066 3.17 5.38
coPapersCiteseer 434 102 1 603 6720 36.94 124.01
coPapersDBLP 540 486 15 245 729 28.21 118.38
great-britain.osm 7 733 822 8 156 517 1.05 91.73
PGPgiantcompo 10 680 24 316 2.28 0.23
power 4 941 6 594 1.33 0.07
Bader, Riedy— STING 17 Jan. 2014 29 / 47
34. Incremental PageRank: Quality for Seed-Set Use
Fagin, et al.’s extension Kendall τ for the top 100 list:
1000 100 10
q q q q q q q q q qq q q q q q q q q qq q q q q q q q q qq q q q q q q q q q qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq0.0000
0.0005
0.0010
0.0015
0.0000
0.0005
0.0010
0.0015
0.0000
0.0005
0.0010
0.0015
0.0000
0.0005
0.0010
0.0015
0.0000
0.0005
0.0010
0.0015
0.0000
0.0005
0.0010
0.0015
caidaRouterLevelcoPapersCiteseercoPapersDBLPgreat_britain.osmPGPgiantcompopower
0 2500 5000 7500 10000 0 2500 5000 7500 10000 0 2500 5000 7500 10000
"Time"
K_0(top100)
Graph name q caidaRouterLevel coPapersCiteseer coPapersDBLP great_britain.osm PGPgiantcompo power
Held vertex threshold / 10**−12 q q q q10000.0 1000.0 100.0 10.0
Smaller is more similar, min. value one. All values here in [0, 0.0018).
Bader, Riedy— STING 17 Jan. 2014 32 / 47
35. Motivation: Graph Algorithms for Analysis
STING
Review of Accomplishments
Selected Details on Recent Work
Related Projects (Future Plans)
Artifacts of the Intel Non-Numeric Computing Program
Bader, Riedy— STING 17 Jan. 2014 33 / 47
36. GRATEFUL: Graph Analysis Tackling Power Efficiency,
Uncertainty, and Locality
David A. Bader (PI), Jason Riedy (co-PI)
Under DARPA PERFECT (Power Efficiency Revolution for Embedded Computing Technologies) #HR0011-13-2-0001
Goal: Improve ops/watt by factor of 50×–75×!
Providing mission-critical analysis of semantic data where
needed:
as close to the data and data use as possible
using the least power necessary to
enable rapid decisions with reliable analysis.
GRATEFUL is developing
new algorithms providing new, fundamental capabilities
while coping with real-world uncertainties
under run-time power and environmental constraints.
STING: Static ⇒ dynamic saves power, lowers latency.
Bader, Riedy— STING 17 Jan. 2014 34 / 47
37. The XScala Project: A Community
Repository for Model-driven Design and
Tuning of Data- Intensive Applications for
Extreme-scale Accelerator-based Systems
David A. Bader (PI, GT), Viktor Prasanna (PI, USC),
Richard Vuduc (co-PI, GT), Jason Riedy (co-PI, GT)
NSF ACI-1339745, an SI2-SSI project
Goal: Accelerate science!
Map irregular data problems onto “accelerators” and make
them usable
Science accelerators: multi-threaded, multi-core; GPGPU;
cloud; etc.
Develop models and analysis kernels together.
Not just graphs: also strings, machine learning, ...
Not just GPUs: also parallelizing sequential roadblocks,
mapping onto FPGAs, finding opportunities for platforms, ...
Bader, Riedy— STING 17 Jan. 2014 35 / 47
38. Graph Algorithm Primitives
Community standards effort underway: Timothy Mattson,
Jeremy Kepner, John Gilbert, Aydin Buluç, ...)
State of the art for “Graphs in the
language of Linear algebra” is mature
enough to support a standard set of
BLAS.
Benefits of this standard include:
Separation of concerns spares graph
algorithm experts the pain of producing
optimized primitives (the vendors will
produce these ... just as with traditional
BLAS)
Prevents mass scale “reinvention of the
wheel” with each graph research group
creating their own primitives.
Bader, Riedy— STING 17 Jan. 2014 36 / 47
39. Higher-Level (aka Fuzzier) Plans
More integration into entire analysis tool chains.
Continue partnering with ETL researchers (e.g.
GraphBuilder), visualization researchers, area experts.
Keep STING focused on the graph mapping and algorithms
portion.
Learn how/what to forget. Cannot keep all data.
Make writing high-performance STING kernels easier:
Looking into Julia (julialang.org) for dynamically compiled
kernels, although large memory footprint.
Keeping an eye on other projects (many related through
GRATEFUL, e.g. SEJITS in UCB’s ASPIRE)
PGAS for distributed memory platforms.
Wants a finer-grained interconnect with higher injection
rates than Ethernet or IB...
And a PGAS mmap... (aka system-level work)
Bader, Riedy— STING 17 Jan. 2014 37 / 47
40. Motivation: Graph Algorithms for Analysis
STING
Review of Accomplishments
Selected Details on Recent Work
Related Projects (Future Plans)
Artifacts of the Intel Non-Numeric Computing Program
Bader, Riedy— STING 17 Jan. 2014 38 / 47
41. Software & Data
Where & What
STING/STINGER http://www.cc.gatech.edu/stinger/; framework
for dynamic graph analysis.
community-el http://www.cc.gatech.edu/~jriedy/
community-detection/ or
http://gitorious.org/community-el; separate static
community detection code, simple input, no external
dependencies.
PASQUAL http://www.cc.gatech.edu/pasqual/;
shared-memory scalable and dependable de novo
assembler
DIMACS Challenge http://www.cc.gatech.edu/dimacs10/; data,
workshop papers and presentations
All software under a modified BSD license.
Bader, Riedy— STING 17 Jan. 2014 39 / 47
42. Publications I
Note: Not including presentations, posters, or publications that
simply used mirasol...
[1] D. Ediger, K. Jiang, E. J. Riedy, and D. A. Bader, “Massive
streaming data analytics: A case study with clustering
coefficients,” in Proc. Workshop on Multithreaded
Architectures and Applications (MTAAP), Atlanta, Georgia,
Apr. 2010.
[2] D. Ediger, E. J. Riedy, D. A. Bader, and H. Meyerhenke,
“Tracking structure of streaming social networks,” in Proc.
Workshop on Multithreaded Architectures and Applications
(MTAAP), Anchorage, Alaska, May 2011.
Bader, Riedy— STING 17 Jan. 2014 40 / 47
43. Publications II
[3] E. J. Riedy, H. Meyerhenke, D. Ediger, and D. A. Bader,
“Parallel community detection for massive graphs,” in
Proceedings of the 9th International Conference on Parallel
Processing and Applied Mathematics, Torun, Poland, Sep.
2011.
[4] ——, “Parallel community detection for massive graphs,” in
10th DIMACS Implementation Challenge Workshop - Graph
Partitioning and Graph Clustering. Atlanta, Georgia:
(workshop paper), Feb. 2012, won first place in the Mix
Challenge and Mix Pareto Challenge.
[5] E. J. Riedy, D. A. Bader, and H. Meyerhenke, “Scalable
multi-threaded community detection in social networks,” in
Workshop on Multithreaded Architectures and Applications
(MTAAP), Shanghai, China, May 2012.
Bader, Riedy— STING 17 Jan. 2014 41 / 47
44. Publications III
[6] R. McColl, O. Green, and D. Bader, “A new parallel algorithm
for connected components in dynamic graphs,” in The 20th
Annual IEEE International Conference on High Performance
Computing (HiPC), Hyderabad, India, Dec. 2013.
[7] E. J. Riedy and D. A. Bader, “Scalable multi-threaded
community detection in social networks,” in 7th Workshop
on Multithreaded Architectures and Applications (MTAAP),
Boston, MA, May 2013.
[8] O. Green, R. McColl, , and D. Bader, “A fast algorithm for
streaming betweenness centrality,” in 4th ASE/IEEE
International Conference on Social Computing (SocialCom),
Amsterdam, The Netherlands, Sep. 2012.
Bader, Riedy— STING 17 Jan. 2014 42 / 47
45. Publications IV
[9] O. Green and D. A. Bader, “A fast algorithm for streaming
betweenness centrality,” in 5th ASE/IEEE International
Conference on Social Computing (SocialCom), Washington,
DC, Sep. 2013.
[10] J. Riedy, H. Meyerhenke, D. A. Bader, D. Ediger, and T. G.
Mattson, “Analysis of streaming social networks and graphs
on multicore architectures,” in IEEE International
Conference on Acoustics, Speech and Signal Processing
(ICASSP), Kyoto, Japan, Mar. 2012. [Online]. Available:
http://www.slideshare.net/jasonriedy/
icassp-2012-analysis-of-streaming-social-networks-and-graph
Bader, Riedy— STING 17 Jan. 2014 43 / 47
46. Publications V
[11] D. Ediger, R. McColl, J. Riedy, and D. A. Bader, “STINGER:
High performance data structure for streaming graphs,” in
The IEEE High Performance Extreme Computing
Conference (HPEC), Waltham, MA, Sep. 2012, best paper
award.
[12] D. A. Bader, H. Meyerhenke, P. Sanders, and D. Wagner,
Eds., Graph Partitioning and Graph Clustering. 10th
DIMACS Implementation Challenge Workshop, ser.
Contemporary Mathematics. Georgia Institute of
Technology, Atlanta, GA: American Mathematical Society
and Center for Discrete Mathematics and Theoretical
Computer Science, 2013, no. 588.
Bader, Riedy— STING 17 Jan. 2014 44 / 47
47. Publications VI
[13] X. Liu, P. Pande, H. Meyerhenke, and D. A. Bader,
“PASQUAL: Parallel techniques for next generation genome
sequence assembly,” IEEE Transactions on Parallel &
Distributed Systems, vol. 24, no. 5, pp. 977–986, 2013.
[14] J. Fairbanks, D. Ediger, R. McColl, D. Bader, and E. Gilbert,
“A statistical framework for analyzing streaming graphs,” in
IEEE/ACM International Conference on Advances in Social
Networks Analysis and Mining (ASONAM), Aug. 2013.
Bader, Riedy— STING 17 Jan. 2014 45 / 47
48. People!
GT is an educational institution
Related graduate students:
David Ediger: graduated, Ph.D., at GTRI
Rob McColl: nearing graduation, shared with GTRI
Oded Green: nearing graduation
James Fairbanks
Anita Zakrzewska
Xing Liu: PASQUAL
Pushkar Pande: PASQUAL, graduated, MS
Related post-doc:
Henning Meyerhenke: Juniorprof. Dr. at Karlsruhe Inst. Tech.
Bader, Riedy— STING 17 Jan. 2014 46 / 47