NERSC is the production high-performance computing (HPC) center for the United States Department of Energy (DOE) Office of Science. The center supports over 6,000 users in 600 projects, using a variety of applications in materials science, chemistry, biology, astrophysics, high energy physics, climate science, fusion science, and more.
NERSC deployed the Cori system on over 9,000 Intel® Xeon Phi™ processors. This session describes the optimization strategy for porting codes that target traditional manycore architectures to the processors. We also discuss highlights and lessons learned from the optimization process on 20 applications associated with the NERSC Exascale Science Application Program (NESAP).
NERSC is the production high-performance computing (HPC) center for the United States Department of Energy (DOE) Office of Science. The center supports over 6,000 users in 600 projects, using a variety of applications in materials science, chemistry, biology, astrophysics, high energy physics, climate science, fusion science, and more.
NERSC deployed the Cori system on over 9,000 Intel® Xeon Phi™ processors. This session describes the optimization strategy for porting codes that target traditional manycore architectures to the processors. We also discuss highlights and lessons learned from the optimization process on 20 applications associated with the NERSC Exascale Science Application Program (NESAP).
Deep Learning Fast MRI Using Channel Attention in Magnitude DomainJoonhyung Lee
My presentation on how we participated in the fastMRI Challanege in 2019.
Aside from theoretical considerations, it also explains key implementation issues that arise in all deep learning for MRI such as disk I/O and CPU/GPU load balancing.
Used for presentation at ISBI 2020 Oral session.
Accidentally wrote the title as "Deep Learning Sum-of-Squares Images in Accelerated Parallel MRI". Sorry for the mistake!
Network-aware Data Management for High Throughput Flows Akamai, Cambridge, ...balmanme
As current technology enables faster storage devices and larger interconnect bandwidth, there is a substantial need for novel system design and middleware architecture to address increasing latency, scalability, and throughput requirements. In this talk, I will outline network-aware data management and present solutions based on my past experience in large-scale data migration between remote repositories.
I will first describe my experience in the initial evaluation of 100Gbps network as a part of the Advance Network Initiative project. We needed intense fine-tuning in network, storage, and application layers, to take advantage of the higher network capacity. I will introduce a special data movement prototype, successfully tested in one of the first 100Gbps demonstrations, in which applications map memory blocks for remote data, in contrast to the send/receive semantics.
Within this scope, I will introduce a flexible network reservation algorithm for on-demand bandwidth guaranteed virtual circuit services. Flexible reservations find best path in a time-dependent dynamic network topology to support predictable application performance. I will then present a data-scheduling model with advance provisioning, in which data movement operations are defined with earliest start and latest completion times.
I will conclude my talk with a very brief overview of my other related projects on performance engineering, hyper-converged virtual storage, and optimization in control and data path for virtualized environments.
Sept 28, 2015
Akamai, Cambridge, MA
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-chiu
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Matthew Chiu, Founder of Almond AI, presents the "Designing CNN Algorithms for Real-time Applications" tutorial at the May 2017 Embedded Vision Summit.
The real-time performance of CNN-based applications can be improved several-fold by making smart decisions at each step of the design process – from the selection of the machine learning framework and libraries used to the design of the neural network algorithm to the implementation of the algorithm on the target platform. This talk delves into how to evaluate the runtime performance of a CNN from a software architecture standpoint. It then explains in detail how to build a neural network from the ground up based on the requirements of the target hardware platform.
Chiu shares his ideas on how to improve performance without sacrificing accuracy, by applying recent research on training very deep networks. He also shows examples of how network optimization can be achieved at the algorithm design level by making a more efficient use of weights before the model is compressed via more traditional methods for deployment in a real-time application.
04 accelerating dl inference with (open)capi and posit numbersYutaka Kawai
This was presented by Louis Ledoux and Marc Casas at OpenPOWER summit EU 2019. The original one is uploaded at:
https://static.sched.com/hosted_files/opeu19/1a/presentation_louis_ledoux_posit.pdf
DEEP NEURAL NETWORKS APPLIED TO LOW POWER ONBOARD IMAGE COMPRESSION
Over the past decade, rapid developments in digital technologies and access to space have enabled unprecedented capabilities of monitoring our planet and, more generally, our Universe.
This new space race is pushing for a paradigm shift in order to respond to the ever-increasing challenge of delivering the useful information to the end users. With huge number of satellites, greater spatial and spectral resolutions, higher temporal cadence and shrinking spectrum resources, on-board data reduction becomes not only a cost saving solution but, in many cases also, a key enabling technology to achieve viable missions.
https://atpi.eventsair.com/obpdc2022/
Dyn delivers exceptional Internet Performance. Enabling high quality services requires data centers around the globe. In order to manage services, customers need timely insight collected from all over the world. Dyn uses DataStax Enterprise (DSE) to deploy complex clusters across multiple datacenters to enable sub 50 ms query responses for hundreds of billions of data points. From granular DNS traffic data, to aggregated counts for a variety of report dimensions, DSE at Dyn has been up since 2013 and has shined through upgrades, data center migrations, DDoS attacks and hardware failures. In this webinar, Principal Engineers Tim Chadwick and Rick Bross cover the requirements which led them to choose DSE as their go-to Big Data solution, the path which led to SPARK, and the lessons that we’ve learned in the process.
Java Thread and Process Performance for Parallel Machine Learning on Multicor...Saliya Ekanayake
The growing use of Big Data frameworks on large machines highlights the importance of performance issues and the value of High Performance Computing (HPC) technology. This paper looks carefully at three major frameworks Spark, Flink and Message Passing Interface (MPI) both in scaling across nodes and internally over the many cores inside modern nodes.We focus on the special challenges of the Java Virtual Machine (JVM) using an Intel Haswell HPC cluster with 24 cores per node. Two parallel machine learning algorithms, K-Means clustering and Multidimensional Scaling (MDS) are used in our performance studies. We identify three major issues – thread models, affinity patterns, and communication mechanisms – as factors affecting performance by large factors and show how to optimize them so that Java can match the performance of traditional HPC languages like C. Further we suggest approaches that preserve the user interface and elegant dataflow approach of Flink and Spark but modify the runtime so that these Big Data frameworks can achieve excellent performance and realize the goals of HPCBig Data convergence.
Day 4 - Big Data on AWS - RedShift, EMR & the Internet of ThingsAmazon Web Services
Big Data is everywhere these days. But what is it and how can you use it to fuel your business? Data is as important to organizations as labour and capital, and if organizations can effectively capture, analyze, visualize and apply big data insights to their business goals, they can differentiate themselves from their competitors and outperform them in terms of operational efficiency and the bottom line.
Join this session to understand the different AWS Big Data and Analytics services such as Amazon Elastic MapReduce (Hadoop), Amazon Redshift (Data Warehouse) and Amazon Kinesis (Streaming), when to use them and how they work together.
Reasons to attend:
- Learn how AWS can help you process and make better use of your data with meaningful insights.
- Learn about Amazon Elastic MapReduce and Amazon Redshift, fully managed petabyte-scale data warehouse solutions.
- Learn about real time data processing with Amazon Kinesis.
Deep Learning Fast MRI Using Channel Attention in Magnitude DomainJoonhyung Lee
My presentation on how we participated in the fastMRI Challanege in 2019.
Aside from theoretical considerations, it also explains key implementation issues that arise in all deep learning for MRI such as disk I/O and CPU/GPU load balancing.
Used for presentation at ISBI 2020 Oral session.
Accidentally wrote the title as "Deep Learning Sum-of-Squares Images in Accelerated Parallel MRI". Sorry for the mistake!
Network-aware Data Management for High Throughput Flows Akamai, Cambridge, ...balmanme
As current technology enables faster storage devices and larger interconnect bandwidth, there is a substantial need for novel system design and middleware architecture to address increasing latency, scalability, and throughput requirements. In this talk, I will outline network-aware data management and present solutions based on my past experience in large-scale data migration between remote repositories.
I will first describe my experience in the initial evaluation of 100Gbps network as a part of the Advance Network Initiative project. We needed intense fine-tuning in network, storage, and application layers, to take advantage of the higher network capacity. I will introduce a special data movement prototype, successfully tested in one of the first 100Gbps demonstrations, in which applications map memory blocks for remote data, in contrast to the send/receive semantics.
Within this scope, I will introduce a flexible network reservation algorithm for on-demand bandwidth guaranteed virtual circuit services. Flexible reservations find best path in a time-dependent dynamic network topology to support predictable application performance. I will then present a data-scheduling model with advance provisioning, in which data movement operations are defined with earliest start and latest completion times.
I will conclude my talk with a very brief overview of my other related projects on performance engineering, hyper-converged virtual storage, and optimization in control and data path for virtualized environments.
Sept 28, 2015
Akamai, Cambridge, MA
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2017-embedded-vision-summit-chiu
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Matthew Chiu, Founder of Almond AI, presents the "Designing CNN Algorithms for Real-time Applications" tutorial at the May 2017 Embedded Vision Summit.
The real-time performance of CNN-based applications can be improved several-fold by making smart decisions at each step of the design process – from the selection of the machine learning framework and libraries used to the design of the neural network algorithm to the implementation of the algorithm on the target platform. This talk delves into how to evaluate the runtime performance of a CNN from a software architecture standpoint. It then explains in detail how to build a neural network from the ground up based on the requirements of the target hardware platform.
Chiu shares his ideas on how to improve performance without sacrificing accuracy, by applying recent research on training very deep networks. He also shows examples of how network optimization can be achieved at the algorithm design level by making a more efficient use of weights before the model is compressed via more traditional methods for deployment in a real-time application.
04 accelerating dl inference with (open)capi and posit numbersYutaka Kawai
This was presented by Louis Ledoux and Marc Casas at OpenPOWER summit EU 2019. The original one is uploaded at:
https://static.sched.com/hosted_files/opeu19/1a/presentation_louis_ledoux_posit.pdf
DEEP NEURAL NETWORKS APPLIED TO LOW POWER ONBOARD IMAGE COMPRESSION
Over the past decade, rapid developments in digital technologies and access to space have enabled unprecedented capabilities of monitoring our planet and, more generally, our Universe.
This new space race is pushing for a paradigm shift in order to respond to the ever-increasing challenge of delivering the useful information to the end users. With huge number of satellites, greater spatial and spectral resolutions, higher temporal cadence and shrinking spectrum resources, on-board data reduction becomes not only a cost saving solution but, in many cases also, a key enabling technology to achieve viable missions.
https://atpi.eventsair.com/obpdc2022/
Dyn delivers exceptional Internet Performance. Enabling high quality services requires data centers around the globe. In order to manage services, customers need timely insight collected from all over the world. Dyn uses DataStax Enterprise (DSE) to deploy complex clusters across multiple datacenters to enable sub 50 ms query responses for hundreds of billions of data points. From granular DNS traffic data, to aggregated counts for a variety of report dimensions, DSE at Dyn has been up since 2013 and has shined through upgrades, data center migrations, DDoS attacks and hardware failures. In this webinar, Principal Engineers Tim Chadwick and Rick Bross cover the requirements which led them to choose DSE as their go-to Big Data solution, the path which led to SPARK, and the lessons that we’ve learned in the process.
Java Thread and Process Performance for Parallel Machine Learning on Multicor...Saliya Ekanayake
The growing use of Big Data frameworks on large machines highlights the importance of performance issues and the value of High Performance Computing (HPC) technology. This paper looks carefully at three major frameworks Spark, Flink and Message Passing Interface (MPI) both in scaling across nodes and internally over the many cores inside modern nodes.We focus on the special challenges of the Java Virtual Machine (JVM) using an Intel Haswell HPC cluster with 24 cores per node. Two parallel machine learning algorithms, K-Means clustering and Multidimensional Scaling (MDS) are used in our performance studies. We identify three major issues – thread models, affinity patterns, and communication mechanisms – as factors affecting performance by large factors and show how to optimize them so that Java can match the performance of traditional HPC languages like C. Further we suggest approaches that preserve the user interface and elegant dataflow approach of Flink and Spark but modify the runtime so that these Big Data frameworks can achieve excellent performance and realize the goals of HPCBig Data convergence.
Day 4 - Big Data on AWS - RedShift, EMR & the Internet of ThingsAmazon Web Services
Big Data is everywhere these days. But what is it and how can you use it to fuel your business? Data is as important to organizations as labour and capital, and if organizations can effectively capture, analyze, visualize and apply big data insights to their business goals, they can differentiate themselves from their competitors and outperform them in terms of operational efficiency and the bottom line.
Join this session to understand the different AWS Big Data and Analytics services such as Amazon Elastic MapReduce (Hadoop), Amazon Redshift (Data Warehouse) and Amazon Kinesis (Streaming), when to use them and how they work together.
Reasons to attend:
- Learn how AWS can help you process and make better use of your data with meaningful insights.
- Learn about Amazon Elastic MapReduce and Amazon Redshift, fully managed petabyte-scale data warehouse solutions.
- Learn about real time data processing with Amazon Kinesis.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
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Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
3. • Song Han et al., Learning both Weights and Connections for
Efficient Neural Networks, NIPS 2015
• Song Han et al., Deep Compression, ICLR 2016 Best Paper
• Song Han et al,. EIE: Efficient Inference Engine on Compressed
Deep Neural Network, ISCA 2016
• Song Han et al., DSD: Dense-Sparse-Dense Training for Deep
Neural Networks, ICLR 2017
14. Pruning
Reduce # of Weights
9~13x
• Regularization is Important
• L1 vs L2 ?
Elements ofStatistical Learning byHastieetal.
SongHanetal.,Learning bothWeights andConnections forEfficient Neural Networks, NIPS 2015
15. Pruning
Reduce # of Weights
9~13x
• Regularization is Important
• Before Ratraining, L1 is Better
• After Retraining, L2 is Better
SongHanetal.,Learning bothWeights andConnections for Efficient Neural Networks, NIPS 2015
16. Pruning
Reduce # of Weights
9~13x
• Store Sparse Connections (Index):
• Compressed Sparse Row (CSR)
• 2a+n+1 Numbers