The document discusses the benefits of GPU accelerated computing for molecular dynamics simulations using LAMMPS. Key points include:
1) GPU acceleration provides significant performance boosts over CPU-only systems, with speedups of up to 6x observed.
2) GPU systems can scale well within a node and across multiple nodes on large clusters.
3) GPU accelerated systems use less energy than CPU-only ones, cutting energy usage in half in some tests.
This document discusses GPU acceleration of the AMBER molecular dynamics simulation software. It provides benchmarks showing that GPU acceleration provides significant performance boosts over CPU-only systems, reducing simulation time by up to 31x and energy usage by 65-75%. The NVIDIA K20 GPU delivers the fastest performance yet for AMBER simulations.
1) GPU accelerated computing provides a large performance boost over CPU-only systems for molecular dynamics simulations, while increasing energy efficiency and reducing costs.
2) Tests with GROMACS show that GPU accelerated systems can achieve 2-3x faster performance than CPU-only systems, while using half the energy.
3) Upgrading to NVIDIA's latest Kepler-based K20X GPU delivers up to 45% better performance than previous GPU models, and can increase simulation speed by up to 3x when combined with CPUs.
This document provides information on molecular dynamics (MD) and quantum chemistry applications that have been optimized to take advantage of GPU acceleration. It lists the key applications in each category along with notes on supported features, reported GPU speedups compared to CPU performance, and release status including support for single or multiple GPUs. The applications cover a wide range of computational chemistry and bioinformatics domains and demonstrate the growing use of GPUs for scientific computing.
1) GPU accelerated computing provides significant performance benefits over CPU-only systems, with NAMD simulations running faster on GPU systems in all tests.
2) GPU acceleration results in a large performance boost for a small additional price of the GPU hardware.
3) Energy usage is reduced by half when using GPU acceleration compared to CPU-only systems.
GPU Computing In Higher Education And ResearchDevang Sachdev
NVIDIA Tesla GPUs can accelerate computational research by providing greater performance at lower costs and power requirements compared to CPUs alone. GPUs allow for faster simulation times, higher accuracy, and more research to be conducted. UCLA's physics and astronomy department saw a 20% performance increase with the same power budget by upgrading to Tesla M2090 GPUs. Over 150,000 academic papers have been published on GPU computing, showing its widespread adoption for accelerating science applications.
This document discusses using GPUs for image processing instead of CPUs. It notes that GPUs have much higher peak performance than CPUs, growing from 5,000 triangles/second in 1995 to 350 million triangles/second in 2010. However, GPU programming is more complex than CPUs due to the different architecture and programming model. This can make it harder to implement algorithms on GPUs and to optimize for high efficiency. The document proposes a methodology for GPU acceleration including characterizing algorithms, estimating performance, using models like Roofline to analyze bottlenecks, and benchmarking. It also describes establishing a competence center to help others overcome the challenges of GPU programming.
This document discusses how GPU clusters are accelerating scientific discovery by providing significantly higher performance and energy efficiency compared to CPU-only systems. Several examples are given of scientific applications such as molecular dynamics simulations, weather modeling, computational fluid dynamics, lattice quantum chromodynamics, and protein-DNA docking that have seen 10-100x speedups using GPUs. The world's fastest and most powerful supercomputers like Titan and Blue Waters are incorporating thousands of GPUs to enable exascale performance for open science.
The document describes the specifications and features of the PowerColor PCS+ HD7870 graphics card. It has a 28nm GCN architecture with 1000MHz core speed and 1225MHz memory. It features a professional cooling system with a 92mm fan and heat pipe design that keeps the card running cooler and quieter than competitors. Benchmarks show it performs faster than the GTX560Ti and HD7850, with overclocking headroom. Reviews praise its performance, cooling, and value.
This document discusses GPU acceleration of the AMBER molecular dynamics simulation software. It provides benchmarks showing that GPU acceleration provides significant performance boosts over CPU-only systems, reducing simulation time by up to 31x and energy usage by 65-75%. The NVIDIA K20 GPU delivers the fastest performance yet for AMBER simulations.
1) GPU accelerated computing provides a large performance boost over CPU-only systems for molecular dynamics simulations, while increasing energy efficiency and reducing costs.
2) Tests with GROMACS show that GPU accelerated systems can achieve 2-3x faster performance than CPU-only systems, while using half the energy.
3) Upgrading to NVIDIA's latest Kepler-based K20X GPU delivers up to 45% better performance than previous GPU models, and can increase simulation speed by up to 3x when combined with CPUs.
This document provides information on molecular dynamics (MD) and quantum chemistry applications that have been optimized to take advantage of GPU acceleration. It lists the key applications in each category along with notes on supported features, reported GPU speedups compared to CPU performance, and release status including support for single or multiple GPUs. The applications cover a wide range of computational chemistry and bioinformatics domains and demonstrate the growing use of GPUs for scientific computing.
1) GPU accelerated computing provides significant performance benefits over CPU-only systems, with NAMD simulations running faster on GPU systems in all tests.
2) GPU acceleration results in a large performance boost for a small additional price of the GPU hardware.
3) Energy usage is reduced by half when using GPU acceleration compared to CPU-only systems.
GPU Computing In Higher Education And ResearchDevang Sachdev
NVIDIA Tesla GPUs can accelerate computational research by providing greater performance at lower costs and power requirements compared to CPUs alone. GPUs allow for faster simulation times, higher accuracy, and more research to be conducted. UCLA's physics and astronomy department saw a 20% performance increase with the same power budget by upgrading to Tesla M2090 GPUs. Over 150,000 academic papers have been published on GPU computing, showing its widespread adoption for accelerating science applications.
This document discusses using GPUs for image processing instead of CPUs. It notes that GPUs have much higher peak performance than CPUs, growing from 5,000 triangles/second in 1995 to 350 million triangles/second in 2010. However, GPU programming is more complex than CPUs due to the different architecture and programming model. This can make it harder to implement algorithms on GPUs and to optimize for high efficiency. The document proposes a methodology for GPU acceleration including characterizing algorithms, estimating performance, using models like Roofline to analyze bottlenecks, and benchmarking. It also describes establishing a competence center to help others overcome the challenges of GPU programming.
This document discusses how GPU clusters are accelerating scientific discovery by providing significantly higher performance and energy efficiency compared to CPU-only systems. Several examples are given of scientific applications such as molecular dynamics simulations, weather modeling, computational fluid dynamics, lattice quantum chromodynamics, and protein-DNA docking that have seen 10-100x speedups using GPUs. The world's fastest and most powerful supercomputers like Titan and Blue Waters are incorporating thousands of GPUs to enable exascale performance for open science.
The document describes the specifications and features of the PowerColor PCS+ HD7870 graphics card. It has a 28nm GCN architecture with 1000MHz core speed and 1225MHz memory. It features a professional cooling system with a 92mm fan and heat pipe design that keeps the card running cooler and quieter than competitors. Benchmarks show it performs faster than the GTX560Ti and HD7850, with overclocking headroom. Reviews praise its performance, cooling, and value.
The document summarizes the performance of several supercomputers on the Green500 list from 2004 to 2011. It notes that the BG/Q system from 2011 had the highest performance on the list with 205 gigaflops per watt, greatly improving upon the BG/L from 2004 which achieved 0.33 gigaflops per watt. It also highlights that the MinoTauro system, based in Spain, was the highest ranked European system and 7th overall on the November 2011 Green500 list with an efficiency of 1266 megaflops per watt, demonstrating Europe's competitiveness in energy-efficient supercomputing.
The document discusses developments in supercomputing, including the Top500 list which ranks the most powerful supercomputers in the world based on performance on LINPACK benchmarks. It provides details on the increasing performance of supercomputers over time, with the most powerful system in November 2011 being the K computer in Japan performing at 10.5 petaflops. The document also summarizes key details on some of the top 10 supercomputers from the 2011 Top500 list and discusses trends in supercomputing hardware and software.
Riken's Fujitsu K computer is the world's fastest supercomputer, with a peak performance of over 11 petaflops. It uses a homogeneous architecture of over 700,000 SPARC64 VIIIfx processors connected via a high-speed interconnect. Looking ahead, future exascale supercomputers in the 2018 timeframe are projected to have over 1 exaflop of peak performance, use over 1 billion processing cores, and consume around 20 megawatts of power. Significant technological advancements will be required across hardware and software to achieve exascale capabilities.
This document summarizes the key features and specifications of the PowerColor PCS+ HD7950 graphics card. It has a core speed of 880MHz and uses AMD's Graphics Core Next architecture with 1792 stream processors and 3GB of GDDR5 memory. The card features technologies like AMD PowerTune, ZeroCore Power, and Eyefinity 2.0 to maximize performance and energy efficiency. It also has a professional dual-fan cooling system and gold power components to enhance stability during overclocking. Benchmark results show it can outperform Nvidia's GTX 580 and 570 graphics cards.
This document compares the PowerColor Radeon HD7790 graphics card to the NVIDIA GeForce GTX650Ti. It shows that the PowerColor HD7790 outperforms the GTX650Ti in various benchmarks, is 20% faster on average, and supports features like AMD CrossFire technology. It also describes the PowerColor HD7790's innovative cooling system and overclocked models.
This document provides an overview of a new CPU capability called Intel® Speed Select
Technology – Base Frequency (Intel® SST-BF), which is available on select SKUs of 2nd
generation Intel® Xeon® Scalable processor (formerly codenamed Cascade Lake). The
document also includes benchmarking data and instructions on how to enable the
capability.
Value propositions of this capability include:
• Select SKUs of 2nd generation Intel® Xeon® Scalable processor (5218N, 6230N, and
6252N) offer a new capability called Intel® SST-BF.
• Intel® SST-BF allows the CPU to be deployed with an asymmetric core frequency
configuration.
• The placement of key workloads on higher frequency Intel® SST-BF enabled cores
can result in an overall system workload increase and potential overall energy
savings when compared to deploying the CPU with symmetric core frequencies
The Vortex technology allows users to manually adjust the fan and its physical attributes, providing flexibility and customization. It features perforated fan blades that can increase air flow by up to 13% while reducing noise levels. The dual inclined cooling fan design reduces turbulence to help heat spread effectively from hot areas.
This project deals with the warehouse scale computers that power all the internet services which we use today. The project covers the hardware blocks used in a Google WSC. Also, the project deals with the architecture of hardware accelerators such as the Graphical Processing Unit and the Tensor Processing Unit, which is highly useful for the warehouse scale machines to run heavy tasks and also to support application-specific machine learning and deep learning tasks. Also, the project explains about the energy efficiency of the processors used by the Google WSC to achieve high performance. The project also tries to explain about performance enhancement mechanism used by Google WSC.
This document is a shopping cart from Newegg.com containing 27 items totaling $7,728.86 before tax and shipping. The items include computer components like cases, hard drives, graphics cards, memory, motherboards, CPUs, optical drives, and more. Shipping for UPS Guaranteed 3 Day Service is $113.35, bringing the total to $8,479.84. The cart contains several instant savings and mail-in rebates that lower the price of some items.
This document provides specifications and configuration information for the VS300FL5 hot tub system. It includes:
- The model number, software version, and other identifying information for the base PCBA and panels
- Wiring diagrams and descriptions of the DIP switch settings and jumper locations for configuring the system
- Details on optional panels, heaters, and other components that can be used with the base system
- Guidelines for setting up the ozone generator connection and selecting the appropriate voltage
This document provides specifications and configuration details for the VS300FH4 pool and spa control system. Key details include:
- The system model number, software version, and part numbers for core components.
- Wiring diagrams and settings for the control board, heaters, pumps, and optional equipment.
- Information on panel configurations and button layouts depending on dip switch settings.
- Guidelines for ozone generator wiring and voltage selection.
The document describes the hardware specifications of the k100 and k25 models from Knome. The k100 is a high-performance processing center with 4 compute nodes, each with 128GB RAM and 128 virtual cores, for a total of 512GB RAM and 512 virtual cores. It also includes a database server and 60TB of storage expandable to 180TB. The k25 is a smaller system aimed at labs with lower volume needs, with one compute node, 256GB RAM, and 24TB of storage. Both systems provide redundancy and fast networking and storage capabilities for genomic data analysis.
This document provides specifications and configuration information for the VS300FH5 pool and spa control system. Key details include:
- The system model number is VSP-VS300FH5-DCAH and the software version is 41.
- Supported base panels include the VL401 LCD panel and VL403 LED panel. Optional base panels include the VL200 and VL240/260 MVP panels.
- Wiring diagrams and DIP switch settings are provided for the standard configuration with a 5.5kW heater.
- Instructions for resetting persistent memory and guidelines for setting up ozone connections are also included.
- Panel overlay and terminal connection information is listed for the VL406
The document describes how the latest Intel® Advanced Vector Extensions 512 (Intel® AVX-512) instructions and Intel® Advanced Encryption Standard New Instructions (Intel® AES-NI) enabled in the latest Intel® 3rd Generation Xeon® Scalable Processor are used to significantly increase and achieve 1 Tb of IPsec throughput.
Highlighted notes of:
Chapter 9: Atomics
Book:
CUDA by Example
An Introduction to General Purpose GPU Computing
Authors:
Jason Sanders
Edward Kandrot
“This book is required reading for anyone working with accelerator-based computing systems.”
–From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory
CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required–just the ability to program in a modestly extended version of C.
CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance.
Table of Contents
Why CUDA? Why Now?
Getting Started
Introduction to CUDA C
Parallel Programming in CUDA C
Thread Cooperation
Constant Memory and Events
Texture Memory
Graphics Interoperability
Atomics
Streams
CUDA C on Multiple GPUs
The Final Countdown
All the CUDA software tools you’ll need are freely available for download from NVIDIA.
Jason Sanders is a senior software engineer in NVIDIA’s CUDA Platform Group, helped develop early releases of CUDA system software and contributed to the OpenCL 1.0 Specification, an industry standard for heterogeneous computing. He has held positions at ATI Technologies, Apple, and Novell.
Edward Kandrot is a senior software engineer on NVIDIA’s CUDA Algorithms team, has more than twenty years of industry experience optimizing code performance for firms including Adobe, Microsoft, Google, and Autodesk.
Presentation I gave at the SORT Conference in 2011. Was generalized from some work I had done with using GPUs to accelerate image processing at FamilySearch.
This document describes using in-place computing on PostgreSQL to perform statistical analysis directly on data stored in a PostgreSQL database. Key points include:
- An F-test is used to compare the variances of accelerometer data from different phone models (Nexus 4 and S3 Mini) and activities (walking and biking).
- Performing the F-test directly in PostgreSQL via SQL queries is faster than exporting the data to an R script, as it avoids the overhead of data transfer.
- PG-Strom, an extension for PostgreSQL, is used to generate CUDA code on-the-fly to parallelize the variance calculations on a GPU, further speeding up the F-test.
This document provides technical specifications for the VS300AV pool and spa control system. It includes the model number, software version, part numbers for various system components, optional panel configurations, wiring diagrams, and DIP switch settings. The document is intended to provide installers and technicians with information needed to properly install and configure the VS300AV system.
Lisa Spelman announced new Intel Xeon Scalable processors including Cascade Lake Advanced Performance processors with up to 48 cores, up to 2666 MHz DDR4 memory, and improved performance on HPC, AI and cloud workloads. Intel Software Guard Extensions provide advanced security capabilities to the new processors. Intel Optane persistent memory delivers affordable high capacity memory and high performance storage to support large scale analytics and database workloads.
The document summarizes steps taken to update the database (db) nodes on an Exadata system from software version 11.2.3.2.1 to 11.2.3.3.0 using the dbnodeupdate.sh utility. It provides details of prerequisites, running the utility in update mode with the -u flag and specified media source, and validations performed during the process including dependency checks. The summary includes checking for known issues, collecting diagnostics, and noting next steps after upgrade completion.
The document discusses NVIDIA data center GPUs such as the A100, A30, A40, and A10 and their performance capabilities. It provides examples of GPU accelerated application performance showing simulations in Simulia CST Studio, Altair CFD, and Rocky DEM achieving excellent speedups on GPUs. It also discusses Paraview visualization being accelerated with NVIDIA OptiX ray tracing, further sped up using RT cores. Looking ahead, the document outlines NVIDIA Grace CPUs which are designed to improve memory bandwidth between CPUs and GPUs for giant AI and HPC models.
This document discusses accelerating dictionary learning algorithms like k-means clustering and Gaussian mixture modeling (GMM) using GPUs and multi-core CPUs. It presents methods to parallelize the computations in k-means and GMM training by expressing the algorithms in matrix formats and performing calculations with linear algebra libraries. Evaluation shows the GPU implementation achieves up to 38x and 209x speedup over single-threaded CPU for k-means and GMM respectively. The work demonstrates GPUs can provide high performance for parallelizable machine learning tasks at lower costs than multi-core servers.
The document summarizes the performance of several supercomputers on the Green500 list from 2004 to 2011. It notes that the BG/Q system from 2011 had the highest performance on the list with 205 gigaflops per watt, greatly improving upon the BG/L from 2004 which achieved 0.33 gigaflops per watt. It also highlights that the MinoTauro system, based in Spain, was the highest ranked European system and 7th overall on the November 2011 Green500 list with an efficiency of 1266 megaflops per watt, demonstrating Europe's competitiveness in energy-efficient supercomputing.
The document discusses developments in supercomputing, including the Top500 list which ranks the most powerful supercomputers in the world based on performance on LINPACK benchmarks. It provides details on the increasing performance of supercomputers over time, with the most powerful system in November 2011 being the K computer in Japan performing at 10.5 petaflops. The document also summarizes key details on some of the top 10 supercomputers from the 2011 Top500 list and discusses trends in supercomputing hardware and software.
Riken's Fujitsu K computer is the world's fastest supercomputer, with a peak performance of over 11 petaflops. It uses a homogeneous architecture of over 700,000 SPARC64 VIIIfx processors connected via a high-speed interconnect. Looking ahead, future exascale supercomputers in the 2018 timeframe are projected to have over 1 exaflop of peak performance, use over 1 billion processing cores, and consume around 20 megawatts of power. Significant technological advancements will be required across hardware and software to achieve exascale capabilities.
This document summarizes the key features and specifications of the PowerColor PCS+ HD7950 graphics card. It has a core speed of 880MHz and uses AMD's Graphics Core Next architecture with 1792 stream processors and 3GB of GDDR5 memory. The card features technologies like AMD PowerTune, ZeroCore Power, and Eyefinity 2.0 to maximize performance and energy efficiency. It also has a professional dual-fan cooling system and gold power components to enhance stability during overclocking. Benchmark results show it can outperform Nvidia's GTX 580 and 570 graphics cards.
This document compares the PowerColor Radeon HD7790 graphics card to the NVIDIA GeForce GTX650Ti. It shows that the PowerColor HD7790 outperforms the GTX650Ti in various benchmarks, is 20% faster on average, and supports features like AMD CrossFire technology. It also describes the PowerColor HD7790's innovative cooling system and overclocked models.
This document provides an overview of a new CPU capability called Intel® Speed Select
Technology – Base Frequency (Intel® SST-BF), which is available on select SKUs of 2nd
generation Intel® Xeon® Scalable processor (formerly codenamed Cascade Lake). The
document also includes benchmarking data and instructions on how to enable the
capability.
Value propositions of this capability include:
• Select SKUs of 2nd generation Intel® Xeon® Scalable processor (5218N, 6230N, and
6252N) offer a new capability called Intel® SST-BF.
• Intel® SST-BF allows the CPU to be deployed with an asymmetric core frequency
configuration.
• The placement of key workloads on higher frequency Intel® SST-BF enabled cores
can result in an overall system workload increase and potential overall energy
savings when compared to deploying the CPU with symmetric core frequencies
The Vortex technology allows users to manually adjust the fan and its physical attributes, providing flexibility and customization. It features perforated fan blades that can increase air flow by up to 13% while reducing noise levels. The dual inclined cooling fan design reduces turbulence to help heat spread effectively from hot areas.
This project deals with the warehouse scale computers that power all the internet services which we use today. The project covers the hardware blocks used in a Google WSC. Also, the project deals with the architecture of hardware accelerators such as the Graphical Processing Unit and the Tensor Processing Unit, which is highly useful for the warehouse scale machines to run heavy tasks and also to support application-specific machine learning and deep learning tasks. Also, the project explains about the energy efficiency of the processors used by the Google WSC to achieve high performance. The project also tries to explain about performance enhancement mechanism used by Google WSC.
This document is a shopping cart from Newegg.com containing 27 items totaling $7,728.86 before tax and shipping. The items include computer components like cases, hard drives, graphics cards, memory, motherboards, CPUs, optical drives, and more. Shipping for UPS Guaranteed 3 Day Service is $113.35, bringing the total to $8,479.84. The cart contains several instant savings and mail-in rebates that lower the price of some items.
This document provides specifications and configuration information for the VS300FL5 hot tub system. It includes:
- The model number, software version, and other identifying information for the base PCBA and panels
- Wiring diagrams and descriptions of the DIP switch settings and jumper locations for configuring the system
- Details on optional panels, heaters, and other components that can be used with the base system
- Guidelines for setting up the ozone generator connection and selecting the appropriate voltage
This document provides specifications and configuration details for the VS300FH4 pool and spa control system. Key details include:
- The system model number, software version, and part numbers for core components.
- Wiring diagrams and settings for the control board, heaters, pumps, and optional equipment.
- Information on panel configurations and button layouts depending on dip switch settings.
- Guidelines for ozone generator wiring and voltage selection.
The document describes the hardware specifications of the k100 and k25 models from Knome. The k100 is a high-performance processing center with 4 compute nodes, each with 128GB RAM and 128 virtual cores, for a total of 512GB RAM and 512 virtual cores. It also includes a database server and 60TB of storage expandable to 180TB. The k25 is a smaller system aimed at labs with lower volume needs, with one compute node, 256GB RAM, and 24TB of storage. Both systems provide redundancy and fast networking and storage capabilities for genomic data analysis.
This document provides specifications and configuration information for the VS300FH5 pool and spa control system. Key details include:
- The system model number is VSP-VS300FH5-DCAH and the software version is 41.
- Supported base panels include the VL401 LCD panel and VL403 LED panel. Optional base panels include the VL200 and VL240/260 MVP panels.
- Wiring diagrams and DIP switch settings are provided for the standard configuration with a 5.5kW heater.
- Instructions for resetting persistent memory and guidelines for setting up ozone connections are also included.
- Panel overlay and terminal connection information is listed for the VL406
The document describes how the latest Intel® Advanced Vector Extensions 512 (Intel® AVX-512) instructions and Intel® Advanced Encryption Standard New Instructions (Intel® AES-NI) enabled in the latest Intel® 3rd Generation Xeon® Scalable Processor are used to significantly increase and achieve 1 Tb of IPsec throughput.
Highlighted notes of:
Chapter 9: Atomics
Book:
CUDA by Example
An Introduction to General Purpose GPU Computing
Authors:
Jason Sanders
Edward Kandrot
“This book is required reading for anyone working with accelerator-based computing systems.”
–From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory
CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required–just the ability to program in a modestly extended version of C.
CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance.
Table of Contents
Why CUDA? Why Now?
Getting Started
Introduction to CUDA C
Parallel Programming in CUDA C
Thread Cooperation
Constant Memory and Events
Texture Memory
Graphics Interoperability
Atomics
Streams
CUDA C on Multiple GPUs
The Final Countdown
All the CUDA software tools you’ll need are freely available for download from NVIDIA.
Jason Sanders is a senior software engineer in NVIDIA’s CUDA Platform Group, helped develop early releases of CUDA system software and contributed to the OpenCL 1.0 Specification, an industry standard for heterogeneous computing. He has held positions at ATI Technologies, Apple, and Novell.
Edward Kandrot is a senior software engineer on NVIDIA’s CUDA Algorithms team, has more than twenty years of industry experience optimizing code performance for firms including Adobe, Microsoft, Google, and Autodesk.
Presentation I gave at the SORT Conference in 2011. Was generalized from some work I had done with using GPUs to accelerate image processing at FamilySearch.
This document describes using in-place computing on PostgreSQL to perform statistical analysis directly on data stored in a PostgreSQL database. Key points include:
- An F-test is used to compare the variances of accelerometer data from different phone models (Nexus 4 and S3 Mini) and activities (walking and biking).
- Performing the F-test directly in PostgreSQL via SQL queries is faster than exporting the data to an R script, as it avoids the overhead of data transfer.
- PG-Strom, an extension for PostgreSQL, is used to generate CUDA code on-the-fly to parallelize the variance calculations on a GPU, further speeding up the F-test.
This document provides technical specifications for the VS300AV pool and spa control system. It includes the model number, software version, part numbers for various system components, optional panel configurations, wiring diagrams, and DIP switch settings. The document is intended to provide installers and technicians with information needed to properly install and configure the VS300AV system.
Lisa Spelman announced new Intel Xeon Scalable processors including Cascade Lake Advanced Performance processors with up to 48 cores, up to 2666 MHz DDR4 memory, and improved performance on HPC, AI and cloud workloads. Intel Software Guard Extensions provide advanced security capabilities to the new processors. Intel Optane persistent memory delivers affordable high capacity memory and high performance storage to support large scale analytics and database workloads.
The document summarizes steps taken to update the database (db) nodes on an Exadata system from software version 11.2.3.2.1 to 11.2.3.3.0 using the dbnodeupdate.sh utility. It provides details of prerequisites, running the utility in update mode with the -u flag and specified media source, and validations performed during the process including dependency checks. The summary includes checking for known issues, collecting diagnostics, and noting next steps after upgrade completion.
The document discusses NVIDIA data center GPUs such as the A100, A30, A40, and A10 and their performance capabilities. It provides examples of GPU accelerated application performance showing simulations in Simulia CST Studio, Altair CFD, and Rocky DEM achieving excellent speedups on GPUs. It also discusses Paraview visualization being accelerated with NVIDIA OptiX ray tracing, further sped up using RT cores. Looking ahead, the document outlines NVIDIA Grace CPUs which are designed to improve memory bandwidth between CPUs and GPUs for giant AI and HPC models.
This document discusses accelerating dictionary learning algorithms like k-means clustering and Gaussian mixture modeling (GMM) using GPUs and multi-core CPUs. It presents methods to parallelize the computations in k-means and GMM training by expressing the algorithms in matrix formats and performing calculations with linear algebra libraries. Evaluation shows the GPU implementation achieves up to 38x and 209x speedup over single-threaded CPU for k-means and GMM respectively. The work demonstrates GPUs can provide high performance for parallelizable machine learning tasks at lower costs than multi-core servers.
GPU computing provides a way to access the power of massively parallel graphics processing units (GPUs) for general purpose computing. GPUs contain over 100 processing cores and can achieve over 500 gigaflops of performance. The CUDA programming model allows programmers to leverage this parallelism by executing compute kernels on the GPU from their existing C/C++ applications. This approach democratizes parallel computing by making highly parallel systems accessible through inexpensive GPUs in personal computers and workstations. Researchers can now explore manycore architectures and parallel algorithms using GPUs as a platform.
This document discusses GPU accelerated computing and programming with GPUs. It provides characteristics of GPUs from Nvidia, AMD, and Intel including number of cores, memory size and bandwidth, and power consumption. It also outlines the 7 steps for programming with GPUs which include building and loading a GPU kernel, allocating device memory, transferring data between host and device memory, setting kernel arguments, enqueueing kernel execution, transferring results back, and synchronizing the command queue. The goal is to achieve super parallel execution with GPUs.
GPU HPC Clusters document discusses GPU cluster research at NCSA including early GPU clusters like QP and Lincoln, follow-up clusters like AC that expanded GPU resources, and eco-friendly cluster EcoG. It describes ISL research in GPU and heterogeneous computing including systems software, runtimes, tools and application development.
NVIDIA GPUs Power HPC & AI Workloads in Cloud with Univainside-BigData.com
In this deck from the Univa Breakfast Briefing at ISC 2018, Duncan Poole from NVIDIA describes how the company is accelerating HPC in the Cloud.
Learn more: https://www.nvidia.com/en-us/data-center/dgx-systems/
and
http://univa.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Today’s groundbreaking scientific discoveries are taking place in HPC data centers. Using containers, researchers and scientists gain the flexibility to run HPC application containers on NVIDIA Volta-powered systems including Quadro-powered workstations, NVIDIA DGX Systems, and HPC clusters.
The document discusses NVIDIA's role in powering the world's fastest supercomputers. It notes that the Summit supercomputer at Oak Ridge National Laboratory is now the fastest system, powered by 27,648 Volta Tensor Core GPUs to achieve over 122 petaflops. NVIDIA GPUs also power 17 of the world's 20 most energy efficient supercomputers, including Europe's fastest Piz Daint and Japan's fastest Fugaku supercomputer. Over 550 applications are now accelerated using NVIDIA GPUs.
CUDA 6.0 provides performance improvements and new features for several CUDA libraries and tools. Key updates include up to 2x faster kernel launches, new cuFFT and cuBLAS features for multi-GPU support, up to 700 GFLOPS performance from cuFFT, over 3 TFLOPS from cuBLAS, and 5x faster cuSPARSE performance compared to MKL. New features also improve the performance of cuRAND, NPP, and Thrust.
The NVIDIA Tesla K80 GPU accelerator delivers significantly faster performance than CPUs and previous GPU models for demanding workloads. It features two GPUs with up to 2.91 TFLOPS double precision and 8.74 TFLOPS single precision performance each, 24GB of total memory, and other advanced capabilities. Benchmark results show the K80 providing up to 2.2x faster performance than the Tesla K20X, up to 2.5x faster than the Tesla K10, and up to 10x faster than CPUs for real-world applications in scientific computing, data analytics, and other fields.
The Power of One: Supermicro’s High-Performance Single-Processor Blade SystemsRebekah Rodriguez
This document summarizes new single-processor blade servers from Supermicro. It introduces the SuperBlade and MicroBlade product lines which offer single-socket performance at lower cost than dual-socket systems while also providing benefits such as reduced software licensing and optimized density. New X12 generation models are detailed including the 6U SuperBlade and MicroBlade which support the latest Intel Xeon E-2300 and Xeon W-3300 processors. Use cases like virtual desktop infrastructure are discussed.
The document describes NVIDIA's NVSwitch chip and DGX-2 server. The NVSwitch chip allows up to 16 GPUs to be fully interconnected with high bandwidth and low latency. It uses a fat-tree network topology to provide 2.4 TBps of bisection bandwidth between the GPUs. The DGX-2 server uses two NVSwitch chips and 16 NVIDIA Tesla V100 GPUs to deliver up to 2000 TFLOPS of deep learning performance within a single 10U rack unit.
This is a presentation I presented at NVIDIA AI Conference in Korea. It's about building the largest GPU - DGX-2, the most powerful supercomputer in one node.
PEER 1 Offers NVIDIA GPU to Accelerate High Performance Applications
PEER 1 has teamed up with NVIDIA the creator of the GPU and a world leader in visual computing, to provide high performance GPU Cloud applications. NVIDIA’s GPUs are well known for making customer software run faster and PEER 1 is offering a number of services that run on NVIDA’s GPUs. PEER 1’s cloud service is built on NVIDIA Telsa GPU’s delivering supercomputing performance in the cloud to solve much tougher problems. Click here to find out how PEER 1 and NVIDIA can transform your business.
Computing Performance: On the Horizon (2021)Brendan Gregg
Talk by Brendan Gregg for USENIX LISA 2021. https://www.youtube.com/watch?v=5nN1wjA_S30 . "The future of computer performance involves clouds with hardware hypervisors and custom processors, servers running a new type of BPF software to allow high-speed applications and kernel customizations, observability of everything in production, new Linux kernel technologies, and more. This talk covers interesting developments in systems and computing performance, their challenges, and where things are headed."
MT58 High performance graphics for VDI: A technical discussionDell EMC World
Hyper-converged infrastructure appliances can enable high end virtualized graphics for all of your users. With proper planning and configuring, the VxRail and Virtual SAN Ready Nodes with Horizon and GPU technology from NVIDIA provide enhanced user experiences. Even the most demanding CAD/CAM “power users” can realize multiple benefits from a virtualized desktop experience. Wyse endpoints complete the end-to-end environment with improved security and rich, rewarding user experiences. Learn best practices, planning, configuration and deployment recommendations to avoid implementation trials and tribulations in this technical session.
DCSF 19 Accelerating Docker Containers with NVIDIA GPUsDocker, Inc.
Using the NVIDIA Container Runtime, many developers and enterprises have been developing, benchmarking and deploying deep learning (DL) frameworks, HPC and other GPU accelerated containers at scale for the last two years. In this talk, we will go over the architecture of the NVIDIA Container Runtime and discuss our recent close collaboration with Docker. The result of our collaboration with Docker is a seamless native integration of the runtime enabling Docker Engine 19.03 CE and the forthcoming Docker Enterprise release to run GPU accelerated containers. We will also highlight containerized NVIDIA drivers. This new feature eliminates the overhead of provisioning GPU machines and brings GPU support on container optimized operating systems, which either lack package managers for installing software or require all applications to run in containers. In this session, you will learn how GPU accelerated containers can be easily built and deployed through the use of driver containers and native support for GPUs in Docker 19.03. The session will include a demo of running a GPU accelerated deep learning container using the new CLI options in Docker 19.03 and containerized drivers. Running NVIDIA GPU accelerated containers with Docker has never been this easy!
This document discusses accelerated computing using GPUs and OpenCL. It begins by covering the evolution of x86 processors towards multi-core designs and the use of GPUs as accelerators. It then introduces accelerated processing units that combine CPU and GPU components. The document concludes by introducing OpenCL as an open standard for programming GPUs and heterogeneous systems that allows developers to write code that scales across CPUs and GPUs.
Our unique 1U GPU servers allow you to use the latest GPUs (Tesla, GTX285, Quadro FX5800) for visualization or offloading processing in a small form factor. These are built on Intel\'s latest Nehalem processors.
The document discusses AMD's technologies for improving energy efficiency and performance in servers and graphics processors. It highlights AMD's Opteron and FireStream processors, which provide high performance while using less power than competitors. AMD is focusing on innovations like its CoolCore technology, 45nm manufacturing process, and support for OpenCL to enhance efficiency and acceleration capabilities across its product lines.
The document discusses NVIDIA's full stack computing platform and ecosystem. It highlights that NVIDIA now has over 2.5 million developers, 24 million CUDA downloads, and powers over 1 billion GPUs. It then summarizes several key technologies in NVIDIA's portfolio including Triton inference serving, Jarvis conversational AI, Maxine virtual collaboration, and developments in HPC and quantum computing simulation.
2. Summary/Conclusions
Benefits of GPU Accelerated Computing
Faster than CPU only systems in all tests
Large performance boost with small marginal price increase
Energy usage cut in half
GPUs scale very well within a node and over multiple nodes
Tesla K20 GPU is our fastest and lowest power high performance GPU to date
Try GPU accelerated LAMMPS for free – www.nvidia.com/GPUTestDrive
3. More Science for Your Money
Embedded Atom Model Blue node uses 2x E5-2687W (8 Cores
6 and 150W per CPU).
5.5
Green nodes have 2x E5-2687W and 1
5 or 2 NVIDIA K10, K20, or K20X GPUs (235W).
Speedup Compared to CPU Only
4.5
4
3.3
2.92
3
2.47
2 1.7
1
0
CPU Only CPU + 1x CPU + 1x CPU + 1x CPU + 2x CPU + 2x CPU + 2x
K10 K20 K20X K10 K20 K20X
Experience performance increases of up to 5.5x with Kepler GPU nodes.
4. K20X, the Fastest GPU Yet
7 Blue node uses 2x E5-2687W (8 Cores
and 150W per CPU).
6
Green nodes have 2x E5-2687W and 2
NVIDIA M2090s or K20X GPUs (235W).
Speedup Relative to CPU Alone
5
4
3
2
1
0
CPU Only CPU + 2x M2090 CPU + K20X CPU + 2x K20X
Experience performance increases of up to 6.2x with Kepler GPU nodes.
One K20X performs as well as two M2090s
5. Get a CPU Rebate to Fund Part of Your GPU Budget
Acceleration in Loop Time Computation by
Additional GPUs
Running NAMD version 2.9
20
18.2
The blue node contains Dual X5670 CPUs
18
(6 Cores per CPU).
16
The green nodes contain Dual X5570 CPUs
Normalized to CPU Only
14 12.9 (4 Cores per CPU) and 1-4 NVIDIA M2090
GPUs.
12
9.88
10
8
6 5.31
4
2
0
1 Node 1 Node + 1x M20901 Node + 2x M20901 Node + 3x M20901 Node + 4x M2090
Increase performance 18x when compared to CPU-only nodes
Cheaper CPUs used with GPUs AND still faster overall performance when
compared to more expensive CPUs!
6. Excellent Strong Scaling on Large Clusters
LAMMPS Gay-Berne 134M Atoms
600
GPU Accelerated XK6
500
CPU only XE6
Loop Time (seconds)
400
3.55x
300
200
3.48x
3.45x
100
0
300 400 500 600 700 800 900
Nodes
From 300-900 nodes, the NVIDIA GPU-powered XK6 maintained 3.5x performance
compared to XE6 CPU nodes
Each blue Cray XE6 Nodes have 2x AMD Opteron CPUs (16 Cores per CPU)
Each green Cray XK6 Node has 1x AMD Opteron 1600 CPU (16 Cores per CPU) and 1x NVIDIA X2090
7. GPUs Sustain 5x Performance for Weak Scaling
Weak Scaling with 32K Atoms per Node
45
40
Loop Time (seconds) 35
30
6.7x 5.8x 4.8x
25
20
15
10
5
0
1 8 27 64 125 216 343 512 729
Nodes
Performance of 4.8x-6.7x with GPU-accelerated nodes
when compared to CPUs alone
Each blue Cray XE6 Node have 2x AMD Opteron CPUs (16 Cores per CPU)
Each green Cray XK6 Node has 1x AMD Opteron 1600 CPU (16 Core per CPU) and 1x NVIDIA X2090
8. Faster, Greener — Worth It!
Energy Consumed in one loop of EAM
140
120 GPU-accelerated computing uses
Lower is better 53% less energy than CPU only
100
Energy Expended (kJ)
80
60
Energy Expended = Power x Time
Power calculated by combining the component’s TDPs
40
20
0
1 Node 1 Node + 1 K20X 1 Node + 2x K20X
Blue node uses 2x E5-2687W (8 Cores and 150W per CPU) and CUDA 4.2.9.
Green nodes have 2x E5-2687W and 1 or 2 NVIDIA K20X GPUs (235W) running CUDA 5.0.36.
9. Molecular Dynamics with LAMMPS
on a Hybrid Cray Supercomputer
W. Michael Brown
National Center for Computational Sciences
Oak Ridge National Laboratory
NVIDIA Technology Theater, Supercomputing 2012
November 14, 2012
13. Recommended GPU Node Configuration for
LAMMPS Computational Chemistry
Workstation or Single Node Configuration
# of CPU sockets 2
Cores per CPU socket 6+
CPU speed (Ghz) 2.66+
System memory per socket (GB) 32
Kepler K10, K20, K20X
GPUs
Fermi M2090, M2075, C2075
# of GPUs per CPU socket 1-2
GPU memory preference (GB) 6
GPU to CPU connection PCIe 2.0 or higher
Server storage 500 GB or higher
Network configuration Gemini, InfiniBand
13 Scale to multiple nodes with same single node configuration
14. GPU Test Drive
Experience GPU Acceleration
For Computational Chemistry
Researchers, Biophysicists
Preconfigured with Molecular
Dynamics Apps
Remotely Hosted GPU Servers
Free & Easy – Sign up, Log in and
See Results
www.nvidia.com/gputestdrive
14
Editor's Notes
CPU OnlyCPU + K10CPU + 2K101k202k201k20x2k20xLoop time: 382.13225115.4154.684.2130.569.9
CPU OnlyCPU + K10CPU + 2K101k202k201k20x2k20xLoop time: 382.13225115.4154.684.2130.569.9
Nodes, box size, atoms, cpu time, cpu+gpu time, gpu speedup11x1x13276842.26.336.67 x82x2x226214441.86.736.21 x273x3x388473641.56.866.05 x644x4x4209715241.57.185.78 x1255x5x5409600041.47.185.77 x2166x6x67077888427.665.48 x3437x7x71123942441.98.345.02 x5128x8x81677721642.38.415.03 x7299x9x92388787242.58.924.76 x
Before we end this session I would like to tell you about GPU Test Drive. It is an excellent resource for computational chemistry researchers such as yourself to evaluate benefits of GPU computing in speeding up your simulations. Most importantly it is free.NVIDIA along with its partners is offering access to remotely hosted GPU cluster. You can run applications such as AMBER and NAMD to find out how your models speed up. You can also try code that you have developed to run on GPU and see how it scales on a 8 GPU cluster. All you need to do is sign up and log in – it is really that easy! We have several partners who are demonstrating the GPU Test Drive on the GTC show floor. Please plan on visiting them.Sign up forms have been given out. If you are interested please fill them out and return them to me.