A Practical Guide to Parallelization in Economics∗
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Analysis of GPU-Libraries for Rapid Prototyping Database Operations
Analysis of GPU-Libraries for Rapid Prototyping Database Operations A look into library support for database operations Harish Kumar Harihara Subramanian Bala Gurumurthy Gabriel Campero Durand David Broneske Gunter Saake University of Magdeburg Magdeburg, Germany fi[email protected] Abstract—Using GPUs for query processing is still an ongoing Usually, library operators for GPUs are either written by research in the database community due to the increasing hardware experts [12] or are available out of the box by device heterogeneity of GPUs and their capabilities (e.g., their newest vendors [13]. Overall, we found more than 40 libraries for selling point: tensor cores). Hence, many researchers develop optimal operator implementations for a specific device generation GPUs each packing a set of operators commonly used in one involving tedious operator tuning by hand. On the other hand, or more domains. The benefits of those libraries is that they are there is a growing availability of GPU libraries that provide constantly updated and tested to support newer GPU versions optimized operators for manifold applications. However, the and their predefined interfaces offer high portability as well as question arises how mature these libraries are and whether they faster development time compared to handwritten operators. are fit to replace handwritten operator implementations not only w.r.t. implementation effort and portability, but also in terms of This makes them a perfect match for many commercial performance. database systems, which can rely on GPU libraries to imple- In this paper, we investigate various general-purpose libraries ment well performing database operators. Some example for that are both portable and easy to use for arbitrary GPUs such systems are: SQreamDB using Thrust [14], BlazingDB in order to test their production readiness on the example of using cuDF [15], Brytlyt using the Torch library [16]. -
Introduction to Openacc 2018 HPC Workshop: Parallel Programming
Introduction to OpenACC 2018 HPC Workshop: Parallel Programming Alexander B. Pacheco Research Computing July 17 - 18, 2018 CPU vs GPU CPU : consists of a few cores optimized for sequential serial processing GPU : has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously GPU enabled applications 2 / 45 CPU vs GPU CPU : consists of a few cores optimized for sequential serial processing GPU : has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously GPU enabled applications 2 / 45 CPU vs GPU CPU : consists of a few cores optimized for sequential serial processing GPU : has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously GPU enabled applications 2 / 45 CPU vs GPU CPU : consists of a few cores optimized for sequential serial processing GPU : has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously GPU enabled applications 2 / 45 3 / 45 Accelerate Application for GPU 4 / 45 GPU Accelerated Libraries 5 / 45 GPU Programming Languages 6 / 45 What is OpenACC? I OpenACC Application Program Interface describes a collection of compiler directive to specify loops and regions of code in standard C, C++ and Fortran to be offloaded from a host CPU to an attached accelerator. I provides portability across operating systems, host CPUs and accelerators History I OpenACC was developed by The Portland Group (PGI), Cray, CAPS and NVIDIA. I PGI, Cray, and CAPs have spent over 2 years developing and shipping commercial compilers that use directives to enable GPU acceleration as core technology. -
Conference Program
Ernest N. Morial ConventionConference Center Program • New Orleans, Louisiana HPC Everywhere, Everyday Conference Program The International Conference for High Performance Exhibition Dates: Computing, Networking, Storage and Analysis November 17-20, 2014 Sponsors: Conference Dates: November 16-21, 2014 Table of Contents 3 Welcome from the Chair 67 HPC Impact Showcase/ Emerging Technologies 4 SC14 Mobile App 82 HPC Interconnections 5 General Information 92 Keynote/Invited Talks 9 SCinet Contributors 106 Papers 11 Registration Pass Access 128 Posters 13 Maps 152 Tutorials 16 Daily Schedule 164 Visualization and Data Analytics 26 Award Talks/Award Presentations 168 Workshops 31 Birds of a Feather 178 SC15 Call for Participation 50 Doctoral Showcase 56 Exhibitor Forum Welcome 3 Welcome to SC14 HPC helps solve some of the SC is fundamentally a technical conference, and anyone world’s most complex problems. who has spent time on the show floor knows the SC Exhibits Innovations from our community have program provides a unique opportunity to interact with far-reaching impact in every area of the future of HPC. Far from being just a simple industry science —from the discovery of new exhibition, our research and industry booths showcase recent 67 HPC Impact Showcase/ drugs to precisely predicting the developments in our field, with a rich combination of research Emerging Technologies next superstorm—even investment labs, universities, and other organizations and vendors of all 82 HPC Interconnections banking! For more than two decades, the SC Conference has types of software, hardware, and services for HPC. been the place to build and share the innovations that are 92 Keynote/Invited Talks making these life-changing discoveries possible. -
GPU Computing with Openacc Directives
Introduction to OpenACC Directives Duncan Poole, NVIDIA Thomas Bradley, NVIDIA GPUs Reaching Broader Set of Developers 1,000,000’s CAE CFD Finance Rendering Universities Data Analytics Supercomputing Centers Life Sciences 100,000’s Oil & Gas Defense Weather Research Climate Early Adopters Plasma Physics 2004 Present Time 3 Ways to Accelerate Applications Applications OpenACC Programming Libraries Directives Languages “Drop-in” Easily Accelerate Maximum Acceleration Applications Flexibility 3 Ways to Accelerate Applications Applications OpenACC Programming Libraries Directives Languages CUDA Libraries are interoperable with OpenACC “Drop-in” Easily Accelerate Maximum Acceleration Applications Flexibility 3 Ways to Accelerate Applications Applications OpenACC Programming Libraries Directives Languages CUDA Languages are interoperable with OpenACC, “Drop-in” Easily Accelerate too! Maximum Acceleration Applications Flexibility NVIDIA cuBLAS NVIDIA cuRAND NVIDIA cuSPARSE NVIDIA NPP Vector Signal GPU Accelerated Matrix Algebra on Image Processing Linear Algebra GPU and Multicore NVIDIA cuFFT Building-block Sparse Linear C++ STL Features IMSL Library Algorithms for CUDA Algebra for CUDA GPU Accelerated Libraries “Drop-in” Acceleration for Your Applications OpenACC Directives CPU GPU Simple Compiler hints Program myscience Compiler Parallelizes code ... serial code ... !$acc kernels do k = 1,n1 do i = 1,n2 OpenACC ... parallel code ... Compiler Works on many-core GPUs & enddo enddo Hint !$acc end kernels multicore CPUs ... End Program myscience -
Openacc Course October 2017. Lecture 1 Q&As
OpenACC Course October 2017. Lecture 1 Q&As. Question Response I am currently working on accelerating The GCC compilers are lagging behind the PGI compilers in terms of code compiled in gcc, in your OpenACC feature implementations so I'd recommend that you use the PGI experience should I grab the gcc-7 or compilers. PGI compilers provide community version which is free and you try to compile the code with the pgi-c ? can use it to compile OpenACC codes. New to OpenACC. Other than the PGI You can find all the supported compilers here, compiler, what other compilers support https://www.openacc.org/tools OpenACC? Is it supporting Nvidia GPU on TK1? I Currently there isn't an OpenACC implementation that supports ARM think, it must be processors including TK1. PGI is considering adding this support sometime in the future, but for now, you'll need to use CUDA. OpenACC directives work with intel Xeon Phi is treated as a MultiCore x86 CPU. With PGI you can use the "- xeon phi platform? ta=multicore" flag to target multicore CPUs when using OpenACC. Does it have any application in field of In my opinion OpenACC enables good software engineering practices by Software Engineering? enabling you to write a single source code, which is more maintainable than having to maintain different code bases for CPUs, GPUs, whatever. Does the CPU comparisons include I think that the CPU comparisons include standard vectorisation that the simd vectorizations? PGI compiler applies, but no specific hand-coded vectorisation optimisations or intrinsic work. Does OpenMP also enable OpenMP does have support for GPUs, but the compilers are just now parallelization on GPU? becoming available. -
“GPU in HEP: Online High Quality Trigger Processing”
“GPU in HEP: online high quality trigger processing” ISOTDAQ 15.1.2020 Valencia Gianluca Lamanna (Univ.Pisa & INFN) G.Lamanna – ISOTDAQ – 15/1/2020 Valencia TheWorldin2035 2 The problem in 2035 15/1/2020 Valencia 15/1/2020 – FCC (Future Circular Collider) is only an example Fixed target, Flavour factories, … the physics reach will be defined ISOTDAQ ISOTDAQ – by trigger! What the triggers will look like in 2035? 3 G.Lamanna The trigger in 2035… … will be similar to the current trigger… High reduction factor High efficiency for interesting events Fast decision High resolution …but will be also different… The higher background and Pile Up will limit the ability to trigger 15/1/2020 Valencia 15/1/2020 on interesting events – The primitives will be more complicated with respect today: tracks, clusters, rings ISOTDAQ ISOTDAQ – 4 G.Lamanna The trigger in 2035… Higher energy Resolution for high pt leptons → high-precision primitives High occupancy in forward region → better granularity Higher luminosity track-calo correlation Bunch crossing ID becomes challenging, pile up All of these effects go in the same direction 15/1/2020 Valencia 15/1/2020 More resolution & more granularity more data & more – processing What previously had to be done in hardware may ISOTDAQ ISOTDAQ – now be done in firmware; What was previously done in firmware may now be done in software! 5 G.Lamanna Classic trigger in the future? Is a traditional “pipelined” trigger possible? Yes and no Cost and dimension Getting all data in one place • New links -> data flow • No “slow” -
GPU-Accelerated Applications for HPC Industries| NVIDIA
GPU-ACCELERATED APPLICATIONS GPU‑ACCELERATED APPLICATIONS Accelerated computing has revolutionized a broad range of industries with over three hundred applications optimized for GPUs to help you accelerate your work. CONTENTS 01 Computational Finance 02 Data Science & Analytics 02 Defense and Intelligence 03 Deep Learning 03 Manufacturing: CAD and CAE COMPUTATIONAL FLUID DYNAMICS COMPUTATIONAL STRUCTURAL MECHANICS DESIGN AND VISUALIZATION ELECTRONIC DESIGN AUTOMATION 06 Media and Entertainment ANIMATION, MODELING AND RENDERING COLOR CORRECTION AND GRAIN MANAGEMENT COMPOSITING, FINISHING AND EFFECTS EDITING ENCODING AND DIGITAL DISTRIBUTION ON-AIR GRAPHICS ON-SET, REVIEW AND STEREO TOOLS WEATHER GRAPHICS 10 Oil and Gas 11 Research: Higher Education and Supercomputing COMPUTATIONAL CHEMISTRY AND BIOLOGY NUMERICAL ANALYTICS PHYSICS 16 Safety & Security Computational Finance APPLICATION DESCRIPTION SUPPORTED FEATURES MULTI-GPU SUPPORT Aaon Benfield Pathwise™ Specialized platform for real-time hedging, Spreadsheet-like modeling interfaces, Yes valuation, pricing and risk management Python-based scripting environment and Grid middleware Altimesh’s Hybridizer C# Multi-target C# framework for data parallel C# with translation to GPU or Multi-Core Yes computing. Xeon Elsen Accelerated Secure, accessible, and accelerated back- Web-like API with Native bindings for Yes Computing Engine (TM) testing, scenario analysis, risk analytics Python, R, Scala, C. Custom models and and real-time trading designed for easy data streams are easy to add. integration and rapid development. Global Valuation Esther In-memory risk analytics system for OTC High quality models not admitting closed Yes portfolios with a particular focus on XVA form solutions, efficient solvers based on metrics and balance sheet simulations. full matrix linear algebra powered by GPUs and Monte Carlo algorithms. -
Multi-Threaded GPU Accelerration of ORBIT with Minimal Code
Princeton Plasma Physics Laboratory PPPL- 4996 4996 Multi-threaded GPU Acceleration of ORBIT with Minimal Code Modifications Ante Qu, Stephane Ethier, Eliot Feibush and Roscoe White FEBRUARY, 2014 Prepared for the U.S. Department of Energy under Contract DE-AC02-09CH11466. Princeton Plasma Physics Laboratory Report Disclaimers Full Legal Disclaimer This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors or their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or any third party’s use or the results of such use of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof or its contractors or subcontractors. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Trademark Disclaimer Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof or its contractors or subcontractors. PPPL Report Availability Princeton Plasma Physics Laboratory: http://www.pppl.gov/techreports.cfm Office of Scientific and Technical Information (OSTI): http://www.osti.gov/bridge Related Links: U.S. -
Matrix Computations on the GPU with Arrayfire for Python and C/C++
Matrix Computations on the GPU with ArrayFire for Python and C/C++ by Andrzej Chrzȩszczyk of Jan Kochanowski University Phone: +1.800.570.1941 • [email protected] • www.accelereyes.com The “AccelerEyes” logo is a trademark of AccelerEyes. Foreward by John Melonakos of AccelerEyes One of the biggest pleasures we experience at AccelerEyes is watching programmers develop awesome stuff with ArrayFire. Oftentimes, ArrayFire programmers contribute back to the community in the form of code, examples, or help on the community forums. This document is an example of an extraordinary contribution by an ArrayFire programmer, written entirely by Andrzej Chrzȩszczyk of Jan Kochanowski University. Readers of this document will find it to be a great resource in learning the ins-and-outs of ArrayFire. On behalf of the rest of the community, we thank you Andrzej for this marvelous contribution. Phone: +1.800.570.1941 • [email protected] • www.accelereyes.com The “AccelerEyes” logo is a trademark of AccelerEyes. Foreward by Andrzej Chrzȩszczyk of Jan Kochanowski University In recent years the Graphics Processing Units (GPUs) designed to efficiently manipulate computer graphics are more and more often used to General Purpose computing on GPU (GPGPU). NVIDIA’s CUDA and OpenCL platforms allow for general purpose parallel programming on modern graphics devices. Unfortunately many owners of powerful graphic cards are not experienced programmers and can find these platforms quite difficult. The purpose of this document is to make the first steps in using modern graphics cards to general purpose computations simpler. In the first two chapters we want to present the ArrayFire software library which in our opinion allows to start computations on GPU in the easiest way. -
HPVM: Heterogeneous Parallel Virtual Machine
HPVM: Heterogeneous Parallel Virtual Machine Maria Kotsifakou∗ Prakalp Srivastava∗ Matthew D. Sinclair Department of Computer Science Department of Computer Science Department of Computer Science University of Illinois at University of Illinois at University of Illinois at Urbana-Champaign Urbana-Champaign Urbana-Champaign [email protected] [email protected] [email protected] Rakesh Komuravelli Vikram Adve Sarita Adve Qualcomm Technologies Inc. Department of Computer Science Department of Computer Science [email protected]. University of Illinois at University of Illinois at com Urbana-Champaign Urbana-Champaign [email protected] [email protected] Abstract hardware, and that runtime scheduling policies can make We propose a parallel program representation for heteroge- use of both program and runtime information to exploit the neous systems, designed to enable performance portability flexible compilation capabilities. Overall, we conclude that across a wide range of popular parallel hardware, including the HPVM representation is a promising basis for achieving GPUs, vector instruction sets, multicore CPUs and poten- performance portability and for implementing parallelizing tially FPGAs. Our representation, which we call HPVM, is a compilers for heterogeneous parallel systems. hierarchical dataflow graph with shared memory and vector CCS Concepts • Computer systems organization → instructions. HPVM supports three important capabilities for Heterogeneous (hybrid) systems; programming heterogeneous systems: a compiler interme- diate representation (IR), a virtual instruction set (ISA), and Keywords Virtual ISA, Compiler, Parallel IR, Heterogeneous a basis for runtime scheduling; previous systems focus on Systems, GPU, Vector SIMD only one of these capabilities. As a compiler IR, HPVM aims to enable effective code generation and optimization for het- 1 Introduction erogeneous systems. -
Openacc Getting Started Guide
OPENACC GETTING STARTED GUIDE Version 2018 TABLE OF CONTENTS Chapter 1. Overview............................................................................................ 1 1.1. Terms and Definitions....................................................................................1 1.2. System Prerequisites..................................................................................... 2 1.3. Prepare Your System..................................................................................... 2 1.4. Supporting Documentation and Examples............................................................ 3 Chapter 2. Using OpenACC with the PGI Compilers...................................................... 4 2.1. OpenACC Directive Summary........................................................................... 4 2.2. CUDA Toolkit Versions....................................................................................6 2.3. C Structs in OpenACC....................................................................................8 2.4. C++ Classes in OpenACC.................................................................................9 2.5. Fortran Derived Types in OpenACC...................................................................13 2.6. Fortran I/O............................................................................................... 15 2.6.1. OpenACC PRINT Example......................................................................... 15 2.7. OpenACC Atomic Support............................................................................. -
Introduction to GPU Programming with CUDA and Openacc
Introduction to GPU Programming with CUDA and OpenACC Alabama Supercomputer Center 1 Alabama Research and Education Network Contents Topics § Why GPU chips and CUDA? § GPU chip architecture overview § CUDA programming § Queue system commands § Other GPU programming options § OpenACC programming § Comparing GPUs to other processors 2 What is a GPU chip? GPU § A Graphic Processing Unit (GPU) chips is an adaptation of the technology in a video rendering chip to be used as a math coprocessor. § The earliest graphic cards simply mapped memory bytes to screen pixels – i.e. the Apple ][ in 1980. § The next generation of graphics cards (1990s) had 2D rendering capabilities for rendering lines and shaded areas. § Graphics cards started accelerating 3D rendering with standards like OpenGL and DirectX in the early 2000s. § The most recent graphics cards have programmable processors, so that game physics can be offloaded from the main processor to the GPU. § A series of GPU chips sometimes called GPGPU (General Purpose GPU) have double precision capability so that they can be used as math coprocessors. 3 Why GPUs? GPU Comparison of peak theoretical GFLOPs and memory bandwidth for NVIDIA GPUs and Intel CPUs over the past few years. Graphs from the NVIDIA CUDA C Programming Guide 4.0. 4 CUDA Programming Language CUDA The GPU chips are massive multithreaded, manycore SIMD processors. SIMD stands for Single Instruction Multiple Data. Previously chips were programmed using standard graphics APIs (DirectX, OpenGL). CUDA, an extension of C, is the most popular GPU programming language. CUDA can also be called from a C++ program. The CUDA standard has no FORTRAN support, but Portland Group sells a third party CUDA FORTRAN.