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Revolutionizing High Performance Computing Nvidia® Tesla™ Revolutionizing High Performance Computing / NVIDIA® TESLATM

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Revolutionizing High Performance Computing Nvidia® Tesla™ Revolutionizing High Performance Computing / NVIDIA® TESLATM REVOLUTIONIZING HIGH PERFORMANCE COMPUTING NVIDIA® TESLA™ REVOLUTIONIZING HIGH PERFORMANCE COMPUTING / NVIDIA® TESLATM TABLE OF CONTENTS REVOLUTIONIZING HIGH GPUs are revolutionizing computing 4 PERFORMANCE COMPUTING GPU architecture and developer tools 6 ® ™ Accelerate your code with OpenACC NVIDIA TESLA 8 Directives GPU acceleration for science and research 10 GPU acceleration for MatLAB users 12 NVIDIA GPUs for designers and 14 engineers GPUs accelerate CFD and Structural 16 mechanics MSC Nastran 2012 CST and weather codes 18 Tesla™ Bio Workbench: Amber on GPUs 20 GPU accelerated bio-informatics 22 GPUs accelerate molecular dynamics 24 and medical imaging GPU computing case studies: 26 The need for computation in the high Oil and Gas performance computing (HPC) Industry GPU computing case studies: 28 is increasing, as large and complex Finance and Supercomputing computational problems become commonplace across many industry Tesla GPU computing solutions 30 segments. Traditional CPU technology, GPU computing solutions: 32 however, is no longer capable of Tesla C scaling in performance sufficiently to GPU computing solutions: 34 address this demand. Tesla Modules The parallel processing capability of the GPU allows it to divide complex computing tasks into thousands of smaller tasks that can be run concurrently. This ability is enabling computational scientists and researchers to address some of the world’s most challenging computational problems up to several orders of magnitude faster. 2 3 REVOLUTIONIZING HIGH PERFORMANCE COMPUTING / NVIDIA® TESLATM THE WORLD’S TOP 5 SUPERCOMPUTERS COST EFFECTIVENESS CHART: TFLOPS PER 1 MILLION $ OF INVESTMENT s s s s s NVIDIA® TESLA™ GPU COMPUTING TFLOP TFLOP TFLOP TFLOP TFLOP 8190 2570 1760 1270 1190 REVOLUTIONIZING HIGH PERFORMANCE COMPUTING 40 10 30 7,5 E egawatt * M The high performance computing R We not only created The rise of GPU 20 5 A (HPC) industry’s need for computation * illion $ of investment s M ower, ower, the world‘s fastest computer, supercomputers on the P is increasing, as large and complex but also implemented a Green500 signifies that EVOLUTIONIZING GPU R COMPUTING computational problems become s per 1 10 2,5 Supercomputers with NVIDIA GPU heterogeneous computing commonplace across many industry heterogeneous systems, Other Supercomputers... CPU based architecture incorporating segments. Traditional CPU technology, built with both GPUs TFLOP “ “ Power consumption however, is no longer capable of scaling 0 0 CPU and GPU, this is a new and CPUs, deliver the innovation.” in performance sufficiently to address highest performance and “K” COMPUTER TIANHE-1A JAGUAR NEBULAE TSUBAME 2.0 this demand. Premier Wen Jiabao unprecedented energy * All data is taken from Top500 list and Media announcements open to public. People’s Republic of China The parallel processing capability of efficiency,” the GPU allows it to divide complex said Wu-chun Feng, computing tasks into thousands founder of the Green500 and of smaller tasks that can be run associate professor of concurrently. This ability is enabling Computer Science at Virginia Tech. GPU SUPERCOMPUTING - GREEN HPC computational scientists and WHY GPU COMPUTING? researchers to address some of the With the ever-increasing demand for more GPUs significantly increase overall system As sequential processors, CPUs are not world’s most challenging computational computing performance, systems based efficiency as measured by performance designed for this type of computation, problems up to several orders of on CPUs alone can no longer keep up. The per watt. “Top500” supercomputers based but they are adept at more serial based magnitude faster. CPU-only systems can only get faster by on heterogeneous architectures are, on tasks such as running operating systems This advancement represents a dramatic adding thousands of individual computers average, almost three times more power- and organizing data. NVIDIA believes in shift in HPC. In addition to dramatic – this method consumes too much efficient than non-heterogeneous systems. applying the most relevant processor to the improvements in speed, GPUs also power and makes supercomputers very This is also reflected on Green500 list – the specific task in hand. consume less power than conventional GPUs DELIVER SUPERIOR PERFORMANCE IN expensive. A different strategy is parallel icon of Eco-friendly supercomputing. CPU-only clusters. GPUs deliver PARALLEL COMPUTATION. computing, and the HPC industry is moving performance increases of 10x to 100x GPUs deliver superior performance in parallel computation. toward a hybrid computing model, where to solve problems in minutes instead of CORE COMPARISON BETWEEN A CPU AND A GPU GPUs and CPUs work together to perform hours–while outpacing the performance general purpose computing tasks. of traditional computing with x86-based 1200 CPUs alone. As parallel processors, GPUs excel at tackling large amounts of similar data Figure 2: The new computing model 1000 CPU From climate modeling to advances in because the problem can be split into includes both a multi-core CPU and a GPU medical tomography, NVIDIA® Tesla™ hundreds or thousands of pieces and GPU with hundreds of cores. 800 GPUs are enabling a wide variety of calculated simultaneously. segments in science and industry to 600 progress in ways that were previously impractical, or even impossible, due to 400 technological limitations. igaflops/sec (double precision) (double igaflops/sec 200 G Figure 1: Co-processing refers to Multiple 0 the use of an accelerator, such as a Cores Hundreds of Cores 2007 2008 2009 2010 2011 2012 GPU, to offload the CPU to increase computational efficiency. CPU GPU 5 REVOLUTIONIZING HIGH PERFORMANCE COMPUTING / NVIDIA® TESLATM The next generation CUDA DEVELOPER ECOSYSTEM parallel computing architecture, I believe history will codenamed “Fermi”. E In just a few years, an entire software R record Fermi as a significant RESEARCH & R R milestone.” ecosystem has developed around the EDUCATION LIBRARIES CUDA architecture – from more than INTEGRATED Dave Patterson, Director, Parallel Computing 400 universities worldwide teaching DEVELOPMENT MATHEMATICAL Research Laboratory, U.C. Berkeley Co-author of ENVIRONMENT PACKAGES Computer Architecture: A Quantitative Approach the CUDA programming model, to a CHITECTU “ EVELOPE CUDA R wide range of libraries, compilers, and LANGUAGES CONSULTANTS, S A middleware that help users optimize & APIS TRAINING, & CERTIFICATION D D applications for GPUs. This rich ecosystem has led to faster discovery ALL MAJOR TOOLS GPU AN TOOL PLATFORMS & PARTNERS and simulation in a wide range of fields including mathematics, life sciences, and manufacturing. CUDA PARALLEL COMPUTING Locate a CUDA teaching center near you: Request a CUDA education course at: ARCHITECTURE research.nvidia.com/content/cuda- www.parallel-compute.com CUDA® is NVIDIA’s parallel computing courses-map architecture and enables applications to run large, parallel workloads The current generation CUDA on NVIDIA GPUs. Applications that architecture, codenamed “Fermi”, is DEVELOPERS TOOLS leverage the CUDA architecture can the most advanced GPU computing be developed in a variety of languages architecture ever built. With over three and APIs, including C, C++, Fortran, billion transistors, Fermi is making NVIDIA Parallel Nsight software integrates CPU OpenCL, and DirectCompute. The GPU and CPU co-processing pervasive and GPU development, allowing developers to create optimal GPU-accelerated applications. CUDA architecture contains hundreds by addressing the full-spectrum of Just a few applications that can of cores capable of running many computing applications. With support experience significant performance Request hands-on Parallel Nsight training: thousands of parallel threads, while for C++, GPUs based on the Fermi benefits include ray tracing, finite www.parallel-compute.com the CUDA programming model lets architecture make parallel processing element analysis, high-precision NVIDIA PARALLEL NSIGHT programmers focus on parallelizing easier and accelerate performance on scientific computing, sparse linear DEVELOPMENT ENVIRONMENT FOR their algorithms and not the mechanics a wider array of applications than ever algebra, sorting, and search VISUAL STUDIO GPU-ACCELERATED LIBRARIES of the language. before. algorithms. NVIDIA Parallel Nsight software is the Take advantage of the massively parallel EM Photonics CULA industry’s first development environment computing power of the GPU by using the A GPU-accelerated linear algebra (LA) for massively parallel computing GPU COMPUTING APPLICATIONS GPU-accelerated versions of your existing library that dramatically improves the integrated into Microsoft Visual Studio, libraries. Some examples include: performance of sophisticated mathematics. the world’s most popular development Libraries and Middleware environment for Windows-based NVIDIA Math Libraries MAGMA applications and services. It integrates A collection of GPU-accelerated A collection of open-source, next- Language Solutions Device level APIs CPU and GPU development, allowing libraries—including FFT, BLAS, sparse generation linear algebra libraries developers to create optimal GPU- matrix operations, RNG, performance for heterogeneous GPU-based Java and accelerated applications. primitives for image/signal processing, architectures supporting interfaces to C C++ FORTRAN Phython Direct OpenCL and the Thrust C++ library of high- current LA packages and standards (e.g. Interface Compute Parallel
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