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.
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.
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.
Excessive interrupts can hurt I/O scalability in Xen. The proposals discuss software interrupt throttling and interrupt-less NAPI to reduce interrupt overhead. They also discuss exposing NUMA information to Xen to improve host I/O NUMA awareness and enabling guest I/O NUMA awareness by constructing _PXM methods and extending device assignment policies.
The needs for immediate responsiveness of VMs in the virtualized environments have been on the rise. Several services in SKT also require soft realtime support for virtual machines to substitute the physical machines to achieve high utilization and adaptability. However, consolidated multiple OSes and irregular external events might render the hypervisor infringe on a VM's promptitude. As a solution of this problem, we are improving Xen's credit scheduler by introducing the RT_PRIORITY that guarantees a VM's running at any given point in time as long as credits remains to be burn. It would increase the quality of service and make a VM's behavior predictable on the consolidated environment. In addition, we extend our suggestion to the multi-core environment and even a large number of physical machines by using live migrations.
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.
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.
Excessive interrupts can hurt I/O scalability in Xen. The proposals discuss software interrupt throttling and interrupt-less NAPI to reduce interrupt overhead. They also discuss exposing NUMA information to Xen to improve host I/O NUMA awareness and enabling guest I/O NUMA awareness by constructing _PXM methods and extending device assignment policies.
The needs for immediate responsiveness of VMs in the virtualized environments have been on the rise. Several services in SKT also require soft realtime support for virtual machines to substitute the physical machines to achieve high utilization and adaptability. However, consolidated multiple OSes and irregular external events might render the hypervisor infringe on a VM's promptitude. As a solution of this problem, we are improving Xen's credit scheduler by introducing the RT_PRIORITY that guarantees a VM's running at any given point in time as long as credits remains to be burn. It would increase the quality of service and make a VM's behavior predictable on the consolidated environment. In addition, we extend our suggestion to the multi-core environment and even a large number of physical machines by using live migrations.
The document summarizes performance enhancements and new features in J2SE 5.0, including ergonomics in the Java Virtual Machine to automatically select optimal settings, improved string handling with StringBuilder, enhancements to Java 2D and image I/O, and reduced startup time and memory footprint through class data sharing. Benchmark results show significant performance improvements over J2SE 1.4.2 in SPECjbb2000 and VolanoMark, as well as up to 22% faster startup for applications. Memory footprint is also reduced for applications on various platforms including Windows XP and Linux.
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.
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.
Série grafických karet Lightning společnosti MSI, předního světového výrobce základních desek a grafických karet, si získala skvělé renomé jak mezi pokročilými uživateli, tak ve světových médiích. Nejnovější člen této rodiny, MSI N480GTX Lightning, je šitý na míru pro extrémní přetaktování. MSI představuje unikátní architekturu Power4, která modelu N480GTX Lightning poskytuje nejsilnější a nejstabilnější výkon a výrazně zvyšuje potenciál pro přetaktování. Svědky jedinečných schopností karty byli účastníci a návštěvníci finále MSI MOA2010 v Taipei, kde švédský mistr v přetaktování “elmor” překonal dosavadní světový rekord v 3DMark Vantage. Grafická karta MSI N480GTX Lightning je doslova napěchována exkluzivními funkcemi, včetně nového systému chlazení Twin Frozr III, který je v porovnání s referenčním chladičem schopen uchladit grafické jádro na teplotu o 18 °C nižší. Samozřejmostí je také funkce trojnásobné změny napětí (Triple Overvoltage) pomocí unikátního nástroje pro přetaktování MSI Afterburner.
Performance of three Intel-based SMB servers running Web, email, and database...Principled Technologies
The latest Intel® Xeon® processor E3-1240-based small-to-medium business (SMB) server delivers significantly better performance and performance per watt than previous-generation Intel processor-based SMB servers while running typical office applications. In our tests, the Intel Xeon processor E3-1240-based server delivered up to 495 percent better performance than three previous-generation Intel processor-based servers did. This superior performance, for comparatively little increase in cost, makes the new Intel Xeon processor E3-1240-based server a wise choice for any small business seeking an all-in-one SMB server.
This document provides an overview of Intel® Speed Select Technology – Base Frequency (Intel® SST-BF), which allows select Intel Xeon Scalable processors to operate with an asymmetric core frequency configuration. Enabling Intel® SST-BF and deploying key workloads on the higher frequency cores can increase overall system performance by up to 14.5% while maintaining comparable power consumption. The document outlines how to configure the BIOS and operating system to utilize Intel® SST-BF and use a script to set the core frequencies. Benchmark results demonstrate the performance gains possible when using this capability.
Makoto Yui, Jun Miyazaki, Shunsuke Uemura and Hayato Yamana. ``Nb-GCLOCK: A Non-blocking Buffer Management based on the Generalized CLOCK'',
Proc. ICDE, March 2010.
This document discusses hardware trends and challenges for building exascale computers. It describes the evolution of processor/node architectures including multi-core and many-core designs. Reaching exascale performance will require addressing power consumption, concurrency, scalability, and fault tolerance issues. Evolutionary paths using commodity processors are unlikely to succeed, while aggressive approaches using clean-sheet designs for low-power customized chips may be needed to achieve exascale performance by 2018. International efforts are underway to develop exascale systems, but overcoming technical challenges to efficiently utilize extreme parallelism remains difficult.
The document discusses energy-efficient storage in virtual machine environments. It notes that energy management is challenging due to the separation between the virtual machine monitor (VMM) and guest operating systems (OSs). Two approaches are proposed: early flush notifications from the VMM to VMs to synchronize buffer flushes, and buffering writes from VMs in the VMM when the disk is asleep to extend idle time. Evaluation shows these approaches reduce energy consumption by up to 14.8% compared to standard disk management in environments with multiple VMs.
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
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.
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 is a presentation I gave on impulse at Open Database Camp in Sardegna, Italy last weekend, en then a bit less impulsively at the Inuits igloo.
A word of caution: I included the notes because they contain some extra info, but the presentation was hacked together from several older ones (not all of them my own) so there might be some flukes in there. :)
Better email response time using Microsoft Exchange 2013 with the Dell PowerE...Principled Technologies
In a market where servers can seem the same at a glance, look for the differences. Your email infrastructure choices will directly affect end-user experience for your UC&C applications. Equipped with more drives in its extra drive slots, the Dell PowerEdge R730xd delivered 31.7 percent better Exchange 2013 response times than a similarly configured, current-generation Supermicro server did. With better Microsoft Exchange Server 2013 response times, the PowerEdge R730xd can help deliver an improved experience for users in your organization.
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.
Accelerate Game Development and Enhance Game Experience with Intel® Optane™ T...Intel® Software
The document discusses findings from using Intel Optane technology for game development. It describes how Optane SSDs improved performance by allowing for faster and more efficient parallel processing, multithreading, and streaming of large files during gameplay. Developers saw benefits like faster loading times, smoother streaming, and more efficient exporting and copying of large files and datasets. Rendering a fluid dynamics simulation with over 1 billion particles was accelerated from 17 hours to 6.3 hours by using an Optane SSD.
Improve deep learning inference performance with Microsoft Azure Esv4 VMs wi...Principled Technologies
Newer Esv4 VMs with 2nd Gen Intel Xeon Scalable processors handled deep learning workloads faster than older Esv3 VMs. On image classification and recommendation benchmarks, Esv4 VMs were 3-8x faster. Esv4 VMs improved performance for small, medium, and large workloads due to Intel Deep Learning Boost in the newer processors. Organizations can get insights from data faster by using Esv4 VMs, helping drive innovation.
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.
This document provides documentation for Percona XtraDB Cluster, an open-source high availability and scalability solution for MySQL users. It includes sections on installation from binaries or source code, key features like high availability and multi-master replication, FAQs, how-tos, limitations, and other documentation. Percona XtraDB Cluster provides synchronous replication across multiple MySQL/Percona Server nodes, allowing for high availability and the ability to write to any node.
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.
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 summarizes performance enhancements and new features in J2SE 5.0, including ergonomics in the Java Virtual Machine to automatically select optimal settings, improved string handling with StringBuilder, enhancements to Java 2D and image I/O, and reduced startup time and memory footprint through class data sharing. Benchmark results show significant performance improvements over J2SE 1.4.2 in SPECjbb2000 and VolanoMark, as well as up to 22% faster startup for applications. Memory footprint is also reduced for applications on various platforms including Windows XP and Linux.
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.
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.
Série grafických karet Lightning společnosti MSI, předního světového výrobce základních desek a grafických karet, si získala skvělé renomé jak mezi pokročilými uživateli, tak ve světových médiích. Nejnovější člen této rodiny, MSI N480GTX Lightning, je šitý na míru pro extrémní přetaktování. MSI představuje unikátní architekturu Power4, která modelu N480GTX Lightning poskytuje nejsilnější a nejstabilnější výkon a výrazně zvyšuje potenciál pro přetaktování. Svědky jedinečných schopností karty byli účastníci a návštěvníci finále MSI MOA2010 v Taipei, kde švédský mistr v přetaktování “elmor” překonal dosavadní světový rekord v 3DMark Vantage. Grafická karta MSI N480GTX Lightning je doslova napěchována exkluzivními funkcemi, včetně nového systému chlazení Twin Frozr III, který je v porovnání s referenčním chladičem schopen uchladit grafické jádro na teplotu o 18 °C nižší. Samozřejmostí je také funkce trojnásobné změny napětí (Triple Overvoltage) pomocí unikátního nástroje pro přetaktování MSI Afterburner.
Performance of three Intel-based SMB servers running Web, email, and database...Principled Technologies
The latest Intel® Xeon® processor E3-1240-based small-to-medium business (SMB) server delivers significantly better performance and performance per watt than previous-generation Intel processor-based SMB servers while running typical office applications. In our tests, the Intel Xeon processor E3-1240-based server delivered up to 495 percent better performance than three previous-generation Intel processor-based servers did. This superior performance, for comparatively little increase in cost, makes the new Intel Xeon processor E3-1240-based server a wise choice for any small business seeking an all-in-one SMB server.
This document provides an overview of Intel® Speed Select Technology – Base Frequency (Intel® SST-BF), which allows select Intel Xeon Scalable processors to operate with an asymmetric core frequency configuration. Enabling Intel® SST-BF and deploying key workloads on the higher frequency cores can increase overall system performance by up to 14.5% while maintaining comparable power consumption. The document outlines how to configure the BIOS and operating system to utilize Intel® SST-BF and use a script to set the core frequencies. Benchmark results demonstrate the performance gains possible when using this capability.
Makoto Yui, Jun Miyazaki, Shunsuke Uemura and Hayato Yamana. ``Nb-GCLOCK: A Non-blocking Buffer Management based on the Generalized CLOCK'',
Proc. ICDE, March 2010.
This document discusses hardware trends and challenges for building exascale computers. It describes the evolution of processor/node architectures including multi-core and many-core designs. Reaching exascale performance will require addressing power consumption, concurrency, scalability, and fault tolerance issues. Evolutionary paths using commodity processors are unlikely to succeed, while aggressive approaches using clean-sheet designs for low-power customized chips may be needed to achieve exascale performance by 2018. International efforts are underway to develop exascale systems, but overcoming technical challenges to efficiently utilize extreme parallelism remains difficult.
The document discusses energy-efficient storage in virtual machine environments. It notes that energy management is challenging due to the separation between the virtual machine monitor (VMM) and guest operating systems (OSs). Two approaches are proposed: early flush notifications from the VMM to VMs to synchronize buffer flushes, and buffering writes from VMs in the VMM when the disk is asleep to extend idle time. Evaluation shows these approaches reduce energy consumption by up to 14.8% compared to standard disk management in environments with multiple VMs.
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
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.
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 is a presentation I gave on impulse at Open Database Camp in Sardegna, Italy last weekend, en then a bit less impulsively at the Inuits igloo.
A word of caution: I included the notes because they contain some extra info, but the presentation was hacked together from several older ones (not all of them my own) so there might be some flukes in there. :)
Better email response time using Microsoft Exchange 2013 with the Dell PowerE...Principled Technologies
In a market where servers can seem the same at a glance, look for the differences. Your email infrastructure choices will directly affect end-user experience for your UC&C applications. Equipped with more drives in its extra drive slots, the Dell PowerEdge R730xd delivered 31.7 percent better Exchange 2013 response times than a similarly configured, current-generation Supermicro server did. With better Microsoft Exchange Server 2013 response times, the PowerEdge R730xd can help deliver an improved experience for users in your organization.
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.
Accelerate Game Development and Enhance Game Experience with Intel® Optane™ T...Intel® Software
The document discusses findings from using Intel Optane technology for game development. It describes how Optane SSDs improved performance by allowing for faster and more efficient parallel processing, multithreading, and streaming of large files during gameplay. Developers saw benefits like faster loading times, smoother streaming, and more efficient exporting and copying of large files and datasets. Rendering a fluid dynamics simulation with over 1 billion particles was accelerated from 17 hours to 6.3 hours by using an Optane SSD.
Improve deep learning inference performance with Microsoft Azure Esv4 VMs wi...Principled Technologies
Newer Esv4 VMs with 2nd Gen Intel Xeon Scalable processors handled deep learning workloads faster than older Esv3 VMs. On image classification and recommendation benchmarks, Esv4 VMs were 3-8x faster. Esv4 VMs improved performance for small, medium, and large workloads due to Intel Deep Learning Boost in the newer processors. Organizations can get insights from data faster by using Esv4 VMs, helping drive innovation.
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.
This document provides documentation for Percona XtraDB Cluster, an open-source high availability and scalability solution for MySQL users. It includes sections on installation from binaries or source code, key features like high availability and multi-master replication, FAQs, how-tos, limitations, and other documentation. Percona XtraDB Cluster provides synchronous replication across multiple MySQL/Percona Server nodes, allowing for high availability and the ability to write to any node.
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.
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.
Symposium on HPC Applications – IIT KanpurRishi Pathak
This document discusses power and energy consumption optimization techniques for high-performance computing (HPC) systems. It begins by showing graphs comparing the top 10 systems by performance on the Top500 and Green500 lists. It then discusses trends for exascale systems, including the need for higher performance per watt. The rest of the document outlines various dynamic power management techniques like dynamic voltage and frequency scaling (DVFS) and how they have been implemented on HPC systems to reduce energy usage without significantly impacting performance. It concludes by discussing NPSF's use of power optimization techniques like workload scheduling, node packing, and a feedback-driven policy engine.
The document summarizes the key features and specifications of the Intel Core 2 Duo processor. It is a 64-bit dual-core processor introduced in 2006 as the successor to the Core Duo. Each of its two cores are based on the Pentium M microarchitecture and have shorter pipelines than the previous Pentium 4 architecture, allowing for higher performance at lower clock speeds. The Core 2 Duo provides benefits like dual-core processing, large shared L2 caches, 64-bit support, and other technologies to improve performance over competitors like the AMD Turion 64 X2.
The document summarizes the key features and specifications of the Intel Core 2 Duo processor. It is a 64-bit dual-core processor introduced in 2006 as the successor to the Core Duo. Each of its cores are based on the Pentium M microarchitecture and have shorter pipelines, allowing for higher performance at lower clock speeds compared to previous architectures like the Pentium 4. The Core 2 Duo comes in desktop and notebook versions with performance about 20% lower in notebooks due to lower voltages and bus speeds.
The document summarizes the key features and specifications of the Intel Core 2 Duo processor. It is a 64-bit dual-core processor introduced in 2006 as the successor to the Core Duo. Each of its cores are based on the improved Pentium M microarchitecture and have shorter pipelines, allowing for higher performance at lower clock speeds compared to previous architectures like the Pentium 4. The Core 2 Duo comes in desktop and notebook versions with minor differences in voltage and bus speeds.
Multi-core processors combine two or more independent processors into a single integrated circuit to improve performance. They emerged as a solution to physical limitations threatening single-core processor improvements. By having multiple cores work in parallel, multi-core processors can achieve higher speeds than single-core processors and help address overheating issues. However, fully utilizing multiple cores requires changes to programming methods and not all software is optimized for multi-core systems.
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.
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.
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.
The document discusses several types of processors including Pentium 4, dual-core, and quad-core processors, explaining their features and advantages. Pentium 4 used the NetBurst architecture but faced challenges scaling to higher speeds. Dual-core and quad-core processors place multiple processor cores on a single chip to improve performance through parallel processing while reducing power needs.
The document introduces the National Supercomputer Center in Tianjin (NSCC-TJ) and its TH-1A supercomputer system. It describes that NSCC-TJ is sponsored by the Chinese government to provide high performance computing services. It then provides details about the TH-1A system including its hybrid CPU and GPU architecture, proprietary interconnect network, 262TB of memory and 2PB of storage. It also summarizes the system's software stack including the Kylin Linux operating system, compilers, programming environment and visualization system.
Allegorithmic developed Substance, a middleware for procedurally generating textures on CPUs to reduce texture memory and streaming bottlenecks. Substance uses a node-based graph to procedurally generate textures. It is designed to take advantage of multi-core CPUs through techniques like task parallelism, data parallelism, and lockless synchronization to efficiently generate textures across CPU cores in parallel. Testing showed Substance could utilize 4 CPU cores to generate textures 3.8 times faster than a single core, helping to maintain high framerates during texture streaming.
Q&a on running the elastic stack on kubernetesDaliya Spasova
- The setup looks good overall with dedicated nodes, appropriate resources, and monitoring/alerting configured. A few minor adjustments are recommended.
- Consider using larger instance types like Standard_D8s_v3 for data nodes to handle potential future data growth.
- Add a minimum of two data nodes for redundancy and better distributed indexing performance.
- Test restore process from snapshots at scale to validate restore times meet recovery objectives.
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.
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.
This document discusses GPU-accelerated simulation as a solution to long simulation times and excessive computing resources required for chip design verification. It notes that verification currently takes over half the design cycle for most chips. GPU acceleration provides 10x faster simulation speeds compared to CPU-based simulators by leveraging the parallel processing power of GPUs. RocketSim is presented as a co-simulation solution that works with existing simulators to offload simulation tasks to GPUs for highly accelerated verification while maintaining full debug visibility and support for large designs. Customer testimonials demonstrate speedups of weeks to days and reduced memory usage.
Introduction to Parallel Distributed Computer SystemsMrMaKKaWi
This document provides details about a course on parallel and distributed computing systems. It discusses why studying parallel computing is important due to technological shifts toward multi-core processors. The course will cover foundations of parallel algorithms and programming, and provide hands-on experience using parallel hardware. Students will need basic knowledge of computer architecture and programming to succeed in the course.
The document summarizes the key differences and improvements between AMD's "Bobcat" and "Jaguar" CPU cores. "Jaguar" features over 10% higher core frequencies, over 15% higher instructions per clock, double the core count from two to four cores, and a larger shared 2MB L2 cache compared to "Bobcat's" 512KB per core cache. "Jaguar" also includes additional instruction sets, a larger 40-bit physical address capability, and a 128-bit floating point unit providing better performance and capabilities than "Bobcat".
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 NAMD for free – www.nvidia.com/GPUTestDrive
3. Kepler - Our Fastest Family of GPUs Yet
4.50
ApoA1 Running NAMD version 2.9
4.00
4.00 The blue node contains Dual E5-2687W CPUs
3.57 (8 Cores per CPU).
3.45
3.50
The green nodes contain Dual E5-2687W CPUs (8
2.9x Cores per CPU) and either 1x NVIDIA M2090, 1x K10
3.00 or 1x K20 for the GPU
Nanoseconds/Day
2.63
2.6x
2.50
2.5x
2.00
1.50 1.37 1.9x
1.00
0.50
0.00
1 CPU Node 1 CPU Node + 1 CPU Node + K10 1 CPU Node + K20 1 CPU Node + K20X
Apolipoprotein A1
M2090
GPU speedup/throughput increased from 1.9x (with M2090) to 2.9x (with K20X)
when compared to a CPU only node
3 NAMD Benchmark Report, Revision 2.0, dated Nov. 5, 2012
4. Run NAMD 2.5x Faster with GPUs
3
Running NAMD 2.9 with CUDA 4.0 ECC Off
2.7
2.6
The blue node contains 2x Intel E5-2687W CPUs
2.5 2.4
(8 Cores per CPU)
Speedup Compared to CPU Only
Each green node contains 2x Intel E5-2687W
2 CPUs (8 Cores per CPU) plus 1x NVIDIA K20 GPUs
1.5
1
0.5
0
CPU All Molecules ApoA1 F1-ATPase STMV
Apolipoprotein A1
Gain 2.5x throughput/performance by adding just 1 GPU
when compared to dual CPU performance
4 NAMD Benchmark Report, Revision 2.0, dated Nov. 5, 2012
5. Kepler – Universally Faster
6
Running NAMD version 2.9
The CPU Only node contains Dual E5-2687W CPUs
5 (8 Cores per CPU).
Speedup Compared to CPU Only
5.1x The Kepler nodes contain Dual E5-2687W CPUs (8
4 4.7x Cores per CPU) and 1 or two NVIDIA K10, K20, or
K20X GPUs.
4.3x
F1-ATPase
3
ApoA1
STMV
2.9x
2
2.6x
2.4x
1
0
CPU Only 1x K10 1x K20 1x K20X 2x K10 2x K20 2x K20X
F1-ATPase
| Kepler nodes use Dual CPUs |
The Kepler GPUs accelerate all simulations, up to 5x
Average acceleration printed in bars
6. Outstanding Strong Scaling with Multi-STMV
Running NAMD version 2.9
Each blue XE6 CPU node contains 1x AMD
100 STMV on Hundreds of Nodes 1600 Opteron (16 Cores per CPU).
1.2
Fermi XK6 Each green XK6 CPU+GPU node contains
1x AMD 1600 Opteron (16 Cores per CPU)
1 and an additional 1x NVIDIA X2090 GPU.
CPU XK6
2.7x
Nanoseconds / Day
0.8
2.9x
0.6
0.4
0.2
3.6x
3.8x Concatenation of 100
0 Satellite Tobacco Mosaic Virus
32 64 128 256 512 640 768
# of Nodes
Accelerate your science by 2.7-3.8x when compared to CPU-based supercomputers
7. Replace 3 Nodes with 1 2090 GPU
Running NAMD version 2.9
Each blue node contains 2x Intel Xeon X5550 CPUs
F1-ATPase (4 Cores, $1000 per CPU).
4 CPU Nodes
0.8 9000
0.74 The green node contains 2x Intel Xeon X5550 CPUs
$8,000
1 CPU Node +8000 (4 Cores, $1000 per CPU) and 1x NVIDIA M2090 GPU
0.7 1x M2090 GPUs
0.63 ($2000 each)
7000
0.6
6000
0.5
5000
0.4 $4,000
4000
0.3
3000
0.2
2000
0.1 1000
0 0 F1-ATPase
Nanoseconds/Day Cost
Speedup of 1.2x for 50% the cost
8. K20 - Greener: Twice The Science Per Watt
1200000
Energy Used in Simulating 1 Nanosecond of ApoA1
Running NAMD version 2.9
1000000 Each blue node contains Dual E5-2687W
CPUs (95W, 4 Cores per CPU).
Each green node contains 2x Intel Xeon X5550
Energy Expended (kJ)
800000
CPUs (95W, 4 Cores per CPU) and 2x NVIDIA
Lower is better K20 GPUs (225W per GPU)
600000
Energy Expended
400000
= Power x Time
200000
0
1 Node 1 Node + 2x K20
Cut down energy usage by ½ with GPUs
8 NAMD Benchmark Report, Revision 2.0, dated Nov. 5, 2012
9. Kepler - Greener: Twice The Science/Joule
Energy used in simulating 1 ns of SMTV
250000
Running NAMD version 2.9
The blue node contains Dual E5-2687W CPUs
200000 (150W each, 8 Cores per CPU).
Energy Expended (kJ)
Lower is better The green nodes contain Dual E5-2687W CPUs
(8 Cores per CPU) and 2x NVIDIA K10, K20, or
150000
K20X GPUs (235W each).
Energy Expended
100000
= Power x Time
50000
0
CPU Only CPU + 2 K10s CPU + 2 K20s CPU + 2 K20Xs
Cut down energy usage by ½ with GPUs
Satellite Tobacco Mosaic Virus
10. Recommended GPU Node Configuration for
NAMD 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
10 Scale to multiple nodes with same single node configuration NAMD Benchmark Report, Revision 2.0, dated Nov. 5, 2012
11. 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 NAMD for free – www.nvidia.com/GPUTestDrive
11 NAMD Benchmark Report, Revision 2.0, dated Nov. 5, 2012