The Integrative Role of COW's and Supercomputers in Research

Total Page:16

File Type:pdf, Size:1020Kb

The Integrative Role of COW's and Supercomputers in Research The Integrative Role of COWÕs and Supercomputers in Research and Education Activities Don Morton, Ganesh Prabu, Daniel Sandholdt, Lee Slater Department of Computer Science The University of Montana {morton | gprabu | marist92 | lslater}@cs.umt.edu ABSTRACT: Experiences of researchers and students are presented in the porting of code between a cluster of Linux workstations at The University of Montana and the Cray T3E at the Arctic Region Supercomputing Center. We test the thesis that low-cost workstation environments may be utilized for training, and to develop and debug parallel codes which can ultimately be moved to the Cray T3E with relative ease for realizing high performance gains. We further present ideas on how the computing environments of supercomputers and COW’s might benefit from more commonality. believe the majority of parallel programmers have 1 Introduction insufficient resources to construct such a “supercomputer.” For this reason, powerful machines In the early 1990’s, several pivotal events, revolving such as the Cray T3E will always be high-demand about the availability of low-cost commodity machines for large-scale production runs. Such processors, occurred which forever changed the nature environments often favor the batch user, and, in our of scientific and high-performance computing. The experience, supercomputer centers encourage such long improving line of Intel x86 architectures and the batch jobs in order to achieve high CPU utilization. increasing use of the growing Internet encouraged the Though these centers try to keep a few interactive PE’s development of the Linux operating system, ensuring available, it is often difficult for several programmers that anybody could own a Unix workstation for home to test their code (or debug) in an interactive manner. or business use. During this same time-frame, When it comes time to hold group training sessions in researchers at Oak Ridge National Laboratory (and U. parallel computing, it becomes even more difficult. So, Tennessee at Knoxville) initiated development of PVM, we maintain that COW environments should be used a highly-portable library and environment for the for this sort of interactive parallel computing, allowing construction of message-passing programs on clusters trainees and developers the interactivity (and of workstations (COW’s). In addition to supporting sometimes lack of network delays) they need, while well-known architectures, PVM supported Linux as freeing the MPP CPU’s for the large-scale batch jobs early as 1993. Also during this time, Cray Research, that utilize these expensive resources best. Inc. introduced its MPP Cray T3D based on the low- cost DEC Alpha chip, running its own version of Unix. In this paper, we accept the above thesis and begin to In addition to supporting its own CRAFT environment, explore the issues of integrating COW and MPP the T3D supported PVM (though somewhat different supercomputer resources so that users may migrate from the “standard” PVM). between the environments as effortlessly as possible. We begin with a case-study – a recent graduate-level The convergence of these tracks marked the beginning course in parallel computing in which students are of a healthy, complementary relationship between initially trained on a Linux cluster and, towards the end COW environments and state of the art supercomputers of the semester port their codes to the Cray T3E. Then, such as the Cray MPP series. Though some Linux and we discuss some of the research activities that have Cray supporters have felt somewhat threatened by the been taking place in the past four years using both presence of a “rival,” the reality is that both Linux and Cray MPP systems. Finally, we present our environments hold important niches, and an integration own opinions on how COW’s and supercomputers may of the environments may easily result in a win-win be better integrated for the benefit of all. situation for everyone. Our thesis is that the COW environment is well-suited for training users in concepts of parallel programming and in the development (which often means a lot of 2 Computing Environments debugging) of parallel codes, all at relatively low The author has been working with Linux since 1991 equipment cost. Though some Beowulf clusters have and with the Cray T3D/E series since 1993. With achieved remarkable performance benchmarks, we funding from the National Science Foundation, a poor 1 CUG 1999 Spring Proceedings man’s supercomputer (Linux cluster) was constructed parallel programming on the existing cluster of Linux in 1995 to provide a local environment for education workstations, using PVM, MPI, and High Performance and continued research in parallel computing. This Fortran. Then, through the support of the Arctic funding supported summer residencies at the Arctic Region Supercomputing Center, students would use Region Supercomputing Center. Therefore, there has accounts on the Cray T3E to execute and compare been constant emphasis placed on developing codes programs that were previously run on the Linux cluster. that run on both the Linux cluster and the Cray T3D/E, and to create similar programming environments. UM Scientific Computing Lab Sometimes this means that we don’t use certain features p1 p2 p3 p4 p5 p6 p7 p8 unless they’re available on both platforms. For example, until recently there was no Fortran 90 compiler on the Linux cluster, so any Fortran code was written in Fortran 77 style for both platforms. 100BaseT Hub Likewise, although shmem is a powerful programming LittleHouse.prairie.edu frontend.scinet.prairie.edu tool on the Cray MPP series, we do not use it in elk.prairie.edu “portable” codes (though, sometimes we use scinet.prairie.edu conditional compilation so that if the program is 10BaseT Hub compiled on the Cray, shmem is used, otherwise MPI or PVM). Internet The Linux cluster at The University of Montana (see Figure 1: University of Montana Linux cluster. Figure 1) consists of nine 100MHz Pentiums. One machine, possessing 128 Mbytes of memory, acts as an One of the first parallel programming activities that account and file server for the other eight machines, students engaged in was the PVM implementation of each possessing 64 Mbytes of memory. The machines the n-body problem. In its simplest form, we are connected by a 100Mb Fast Ethernet. Although considered a set of masses in two-dimensions and users may log into any machine (via NIS) and see their calculated the net gravitational force applied to each file systems (via NFS), the primary mode of use is to particle. A quick and dirty parallel algorithm was log in to the server. Since users have valid accounts on introduced so that students could gain experience in each machine through NIS, and their applications can writing their first parallel program that did something be seen by each machine via NFS, there is no need to “useful.” For most students, this was a difficult task, transfer an application to each machine for parallel and they spent a lot of time learning how separate computing. Thus, in many respects, users of the system executables could interact in a nondeterministic can run programs transparently, much as they would on fashion, plus they began to familiarize themselves with a T3E. The system supports PVM, MPI and recently, the mechanics of launching parallel executables. Such Portland Group’s HPF. difficulty would have occurred on either our Linux cluster or the T3E, so it was more appropriate to use The Cray T3E at the Arctic Region Supercomputing local resources for this. Though PVM doesn’t seem as Center consists of 272 450-MHz processors. Until popular as it did in the early 1990’s (a time when it had recently, the network connections to/from ARSC were little competition), it certainly appears in much legacy very slow, making remote use of the system frustrating, code, and it served to introduce students to something which of course increased the need to work on code other than SPMD. locally. Outside network connections have improved substantially since April 1999, but, due to very high Through the semester, students were also introduced to CPU utilization, it still is often more convenient to MPI and HPF, and were required to implement the n- perform training and development activities on the body problem in each of these paradigms. Since the local Linux cluster. students had already been exposed to a message- passing implementation with PVM, an MPI 3 Case Study Ð Parallel Programming implementation was simpler for them, and they enjoyed Course the higher-level constructs provided by that library. Of course, those students who didn’t constrain themselves In the Spring 1999 semester, a graduate-level (primarily to SPMD programming with PVM had to modify their masters degree students) course in parallel processing codes extensively when using MPI. Finally, students was offered at The University of Montana with the had little difficulty modifying their programs for HPF, intent of giving students an applied, hands-on but got a little confused playing around with HPF introduction to parallel computing. One expected compiler directives for optimizing their code. outcome of the course was to test the thesis stated in the Introduction. Students would be initially introduced to 2 CUG 1999 Spring Proceedings At this point, students had written and executed material for this paper. Two lab sessions were programs in PVM, MPI, and HPF, all on the Linux developed – the first session was simply an introduction cluster, and were becoming somewhat proficient in to the use of the T3E environment, in which students writing simple parallel programs. The next step was to were required to compile previously-ported code, and introduce them to readily-available performance then execute, in both interactive and batch (NQS) analysis tools that would run on the Linux cluster, and modes.
Recommended publications
  • PGI Fortran Guide
    PGI® User’s Guide Parallel Fortran, C and C++ for Scientists and Engineers The Portland Group® STMicroelectronics Two Centerpointe Drive Lake Oswego, OR 97035 While every precaution has been taken in the preparation of this document, The Portland Group® (PGI®), a wholly-owned subsidiary of STMicroelectronics, Inc., makes no warranty for the use of its products and assumes no responsibility for any errors that may appear, or for damages resulting from the use of the information contained herein. The Portland Group ® retains the right to make changes to this information at any time, without notice. The software described in this document is distributed under license from STMicroelectronics, Inc. and/or The Portland Group® and may be used or copied only in accordance with the terms of the license agreement ("EULA"). No part of this document may be reproduced or transmitted in any form or by any means, for any purpose other than the purchaser's or the end user's personal use without the express written permission of STMicroelectronics, Inc and/or The Portland Group®. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this manual, STMicroelectronics was aware of a trademark claim. The designations have been printed in caps or initial caps. PGF95, PGF90, and PGI Unified Binary are trademarks; and PGI, PGHPF, PGF77, PGCC, PGC++, PGI Visual Fortran, PVF, Cluster Development Kit, PGPROF, PGDBG, and The Portland Group are registered trademarks of The Portland Group Incorporated. PGI CDK is a registered trademark of STMicroelectronics. *Other brands and names are the property of their respective owners.
    [Show full text]
  • The Portland Group
    ® PGI Compiler User's Guide Parallel Fortran, C and C++ for Scientists and Engineers Release 2011 The Portland Group While every precaution has been taken in the preparation of this document, The Portland Group® (PGI®), a wholly-owned subsidiary of STMicroelectronics, Inc., makes no warranty for the use of its products and assumes no responsibility for any errors that may appear, or for damages resulting from the use of the information contained herein. The Portland Group retains the right to make changes to this information at any time, without notice. The software described in this document is distributed under license from STMicroelectronics and/or The Portland Group and may be used or copied only in accordance with the terms of the end-user license agreement ("EULA"). PGI Workstation, PGI Server, PGI Accelerator, PGF95, PGF90, PGFORTRAN, and PGI Unified Binary are trademarks; and PGI, PGHPF, PGF77, PGCC, PGC++, PGI Visual Fortran, PVF, PGI CDK, Cluster Development Kit, PGPROF, PGDBG, and The Portland Group are registered trademarks of The Portland Group Incorporated. Other brands and names are property of their respective owners. No part of this document may be reproduced or transmitted in any form or by any means, for any purpose other than the purchaser's or the end user's personal use without the express written permission of STMicroelectronics and/or The Portland Group. PGI® Compiler User’s Guide Copyright © 2010-2011 STMicroelectronics, Inc. All rights reserved. Printed in the United States of America First Printing: Release 2011, 11.0, December, 2010 Second Printing: Release 2011, 11.1, January, 2011 Third Printing: Release 2011, 11.2, February, 2011 Fourth Printing: Release 2011, 11.3, March, 2011 Fourth Printing: Release 2011, 11.4, April, 2011 Technical support: [email protected] Sales: [email protected] Web: www.pgroup.com ID: 1196151 Contents Preface .....................................................................................................................................
    [Show full text]
  • The Portland Group
    ® PGI Compiler Reference Manual Parallel Fortran, C and C++ for Scientists and Engineers Release 2011 The Portland Group While every precaution has been taken in the preparation of this document, The Portland Group® (PGI®), a wholly-owned subsidiary of STMicroelectronics, Inc., makes no warranty for the use of its products and assumes no responsibility for any errors that may appear, or for damages resulting from the use of the information contained herein. The Portland Group retains the right to make changes to this information at any time, without notice. The software described in this document is distributed under license from STMicroelectronics and/or The Portland Group and may be used or copied only in accordance with the terms of the end-user license agreement ("EULA"). PGI Workstation, PGI Server, PGI Accelerator, PGF95, PGF90, PGFORTRAN, and PGI Unified Binary are trademarks; and PGI, PGHPF, PGF77, PGCC, PGC++, PGI Visual Fortran, PVF, PGI CDK, Cluster Development Kit, PGPROF, PGDBG, and The Portland Group are registered trademarks of The Portland Group Incorporated. Other brands and names are property of their respective owners. No part of this document may be reproduced or transmitted in any form or by any means, for any purpose other than the purchaser's or the end user's personal use without the express written permission of STMicroelectronics and/or The Portland Group. PGI® Compiler Reference Manual Copyright © 2010-2011 STMicroelectronics, Inc. All rights reserved. Printed in the United States of America First printing: Release 2011, 11.0, December, 2010 Second Printing: Release 2011, 11.1, January 2011 Third Printing: Release 2011, 11.3, March 2011 Fourth Printing: Release 2011, 11.4, April 2011 Fifth Printing: Release 2011, 11.5, May 2011 Technical support: [email protected] Sales: [email protected] Web: www.pgroup.com ID: 111162228 Contents Preface .....................................................................................................................................
    [Show full text]
  • The Portland Group
    PGI Visual Fortran® 2013 Installation Guide Version 13.5 The Portland Group While every precaution has been taken in the preparation of this document, The Portland Group® (PGI®), a wholly-owned subsidiary of STMicroelectronics, Inc., makes no warranty for the use of its products and assumes no responsibility for any errors that may appear, or for damages resulting from the use of the information contained herein. The Portland Group retains the right to make changes to this information at any time, without notice. The software described in this document is distributed under license from STMicroelectronics and/or The Portland Group and may be used or copied only in accordance with the terms of the license agreement ("EULA"). PGI Workstation, PGI Server, PGI Accelerator, PGF95, PGF90, PGFORTRAN, and PGI Unified Binary are trademarks; and PGI, PGHPF, PGF77, PGCC, PGC++, PGI Visual Fortran, PVF, PGI CDK, Cluster Development Kit, PGPROF, PGDBG, and The Portland Group are registered trademarks of The Portland Group Incorporated. Other brands and names are property of their respective owners. No part of this document may be reproduced or transmitted in any form or by any means, for any purpose other than the purchaser's or the end user's personal use without the express written permission of STMicroelectronics and/or The Portland Group. PVF® Installation Guide Copyright © 2010-2013 STMicroelectronics, Inc. All rights reserved. Printed in the United States of America First Printing: Release 2013, version 13.1, January 2013 Second Printing: Release 2013, version 13.2, February 2013 Third Printing: Release 2013, version 13.3, March 2013 Fourth Printing: Release 2013, version 13.4, April 2013 Fourth Printing: Release 2013, version 13.5, May 2013 Technical support: [email protected] Sales: [email protected] Web: www.pgroup.com ID: 07183206 Contents 1.
    [Show full text]
  • The Portland Group
    PGI® 2013 Release Notes Version 13.3 The Portland Group While every precaution has been taken in the preparation of this document, The Portland Group® (PGI®) makes no warranty for the use of its products and assumes no responsibility for any errors that may appear, or for damages resulting from the use of the information contained herein. The Portland Group retains the right to make changes to this information at any time, without notice. The software described in this document is distributed under license from The Portland Group and/or its licensors and may be used or copied only in accordance with the terms of the end-user license agreement ("EULA"). PGI Workstation, PGI Server, PGI Accelerator, PGF95, PGF90, PGFORTRAN, PGI Unified Binary, and PGCL are trademarks; and PGI, PGHPF, PGF77, PGCC, PGC++, PGI Visual Fortran, PVF, PGI CDK, Cluster Development Kit, PGPROF, PGDBG, and The Portland Group are registered trademarks of The Portland Group Incorporated. Other brands and names are property of their respective owners. No part of this document may be reproduced or transmitted in any form or by any means, for any purpose other than the purchaser's or the end user's personal use without the express written permission of The Portland Group, Inc. PGI® 2013 Release Notes Copyright © 2013 The Portland Group, Inc. and STMicroelectronics, Inc. All rights reserved. Printed in the United States of America First Printing: Release 2013, version 13.1, January 2013 Second Printing: Release 2013, version 13.2, February 2013 Third Printing: Release 2013, version 13.3, March 2013 Technical support: [email protected] Sales: [email protected] Web: www.pgroup.com ID: 07135184 Contents 1.
    [Show full text]
  • Tuning C++ Applications for the Latest Generation X64 Processors with PGI Compilers and Tools
    Tuning C++ Applications for the Latest Generation x64 Processors with PGI Compilers and Tools Douglas Doerfler and David Hensinger Sandia National Laboratories Brent Leback and Douglas Miles The Portland Group (PGI) ABSTRACT: At CUG 2006, a cache oblivious implementation of a two dimensional Lagrangian hydrodynamics model of a single ideal gas material was presented. This paper presents further optimizations to this C++ application to allow packed, consecutive-element storage of vectors, some restructuring of loops containing neighborhood operations, and adding type qualifiers to some C++ pointer declarations to improve performance. In addition to restructuring of the application, analysis of the compiler-generated code resulted in improvements to the latest PGI C++ compiler in the area of loop-carried redundancy elimination, resolution of pointer aliasing conflicts, and vectorization of loops containing min and max reductions. These restructuring and compiler optimization efforts by PGI and Sandia have resulted in application speedups of 1.25 to 1.80 on the latest generation of x64 processors. KEYWORDS: Compiler, C, C++, Optimization, Vectorization, Performance Analysis, AMD Opteron™, Intel Core™ 2, Micro-architecture vector or SIMD units to increase FLOP rates in general 1. Introduction purpose processors. While first-generation AMD and Intel 64-bit x86 Although modern compilers do an admirable job of (x64) processors contain 128-bit wide Streaming SIMD optimization when provided with nothing other than the Extensions (SSE) registers, their 64-bit data paths and “–fast” switch, many times there are still significant floating-point units limit the performance benefit of performance gains to be obtained with detailed analysis vectorizing double-precision loops.
    [Show full text]
  • Supercomputing in Plain English Exercise #3: Arithmetic Operations in This Exercise, We’Ll Use the Same Conventions and Commands As in Exercises #1 and #2
    Supercomputing in Plain English Exercise #3: Arithmetic Operations In this exercise, we’ll use the same conventions and commands as in Exercises #1 and #2. You should refer back to the Exercise #1 and #2 descriptions for details on various Unix commands. You MUST complete Exercises #1 and #2 BEFORE starting Exercise #3. For Exercise #3, YOU ARE EXPECTED TO KNOW HOW TO ACCOMPLISH BASIC TASKS, based on your experiences with Exercises #1 and #2. In the exercise, you’ll benchmark various arithmetic operations, using various compilers and levels of compiler optimization. Specifically, you’ll benchmark using the following compilers: the GNU Fortran compiler, gfortran, for various optimization levels; the Intel Fortran compiler, ifort, for various optimization levels; the Portland Group Fortran compiler, pgf90, for various optimization levels. Here are the steps for this exercise: 1. Log in to the Linux cluster supercomputer (sooner.oscer.ou.edu). 2. Copy the ArithmeticOperations directory: % cp -r ~hneeman/SIPE2011_exercises/ArithmeticOperations/ ~/SIPE2011_exercises/ 3. Choose which language you want to use (C or Fortran90), and cd into the appropriate directory: % cd ~/SIPE2011_exercises/ArithmeticOperations/C/ OR: % cd ~/SIPE2011_exercises/ArithmeticOperations/Fortran90/ 4. Edit the batch script arithmetic_operations.bsub so that it contains your username and your e-mail address. 5. Compile, using the shell script named make_cmd (a shell script is a file containing a sequence of Unix commands), which in turn invokes the make command: % make_cmd If that doesn’t work, try this: % ./make_cmd 6. Submit the batch job: % bsub < arithmetic_operations.bsub 7. Once the batch job completes, examine the several output files to see the timings for your runs with executables created by the various compilers under the various levels of optimization.
    [Show full text]
  • PGI Visual Fortran Reference Manual
    ® PGI Visual Fortran Reference Manual Parallel Fortran for Scientists and Engineers Release 2011 The Portland Group While every precaution has been taken in the preparation of this document, The Portland Group® (PGI®), a wholly-owned subsidiary of STMicroelectronics, Inc., makes no warranty for the use of its products and assumes no responsibility for any errors that may appear, or for damages resulting from the use of the information contained herein. The Portland Group retains the right to make changes to this information at any time, without notice. The software described in this document is distributed under license from STMicroelectronics and/or The Portland Group and may be used or copied only in accordance with the terms of the end-user license agreement ("EULA"). PGI Workstation, PGI Server, PGI Accelerator, PGF95, PGF90, PGFORTRAN, and PGI Unified Binary are trademarks; and PGI, PGHPF, PGF77, PGCC, PGC++, PGI Visual Fortran, PVF, PGI CDK, Cluster Development Kit, PGPROF, PGDBG, and The Portland Group are registered trademarks of The Portland Group Incorporated. Other brands and names are property of their respective owners. No part of this document may be reproduced or transmitted in any form or by any means, for any purpose other than the purchaser's or the end user's personal use without the express written permission of STMicroelectronics and/or The Portland Group. PGI® Visual Fortran Reference Manual Copyright © 2010-2012 STMicroelectronics, Inc. All rights reserved. Printed in the United States of America First printing:
    [Show full text]
  • The GPU Computing Revolution
    The GPU Computing Revolution From Multi-Core CPUs to Many-Core Graphics Processors A Knowledge Transfer Report from the London Mathematical Society and Knowledge Transfer Network for Industrial Mathematics By Simon McIntosh-Smith Copyright © 2011 by Simon McIntosh-Smith Front cover image credits: Top left: Umberto Shtanzman / Shutterstock.com Top right: godrick / Shutterstock.com Bottom left: Double Negative Visual Effects Bottom right: University of Bristol Background: Serg64 / Shutterstock.com THE GPU COMPUTING REVOLUTION From Multi-Core CPUs To Many-Core Graphics Processors By Simon McIntosh-Smith Contents Page Executive Summary 3 From Multi-Core to Many-Core: Background and Development 4 Success Stories 7 GPUs in Depth 11 Current Challenges 18 Next Steps 19 Appendix 1: Active Researchers and Practitioner Groups 21 Appendix 2: Software Applications Available on GPUs 23 References 24 September 2011 A Knowledge Transfer Report from the London Mathematical Society and the Knowledge Transfer Network for Industrial Mathematics Edited by Robert Leese and Tom Melham London Mathematical Society, De Morgan House, 57–58 Russell Square, London WC1B 4HS KTN for Industrial Mathematics, Surrey Technology Centre, Surrey Research Park, Guildford GU2 7YG 2 THE GPU COMPUTING REVOLUTION From Multi-Core CPUs To Many-Core Graphics Processors AUTHOR Simon McIntosh-Smith is head of the Microelectronics Research Group at the Univer- sity of Bristol and chair of the Many-Core and Reconfigurable Supercomputing Conference (MRSC), Europe’s largest conference dedicated to the use of massively parallel computer architectures. Prior to joining the university he spent fifteen years in industry where he designed massively parallel hardware and software at companies such as Inmos, STMicro- electronics and Pixelfusion, before co-founding ClearSpeed as Vice-President of Architec- ture and Applications.
    [Show full text]
  • CUDA Fortran 2013
    CUDA Fortran 2013 Brent Leback The Portland Group [email protected] Why Fortran? . Rich legacy in the scientific community . Semantics – easier to vectorize/parallelize . Array descriptors . Modules . Fortran 2003 Today’s Fortran delivers many of the abstraction features of C++ without compromising performance Tesla C1060 Commodity Multicore x86 + Commodity Manycore GPUs 4 – 24 CPU Cores 240 GPU/Accelerator Cores Tesla C2050 “Fermi” 4 – 24 CPU Cores 448 GPU/Accelerator Cores Tesla K20 “Kepler” GPU Programming Constants The Program must: . Initialize/Select the GPU to run on . Allocate data on the GPU . Move data from host, or initialize data on GPU . Launch kernel(s) . Gather results from GPU . Deallocate data 6 What Does CUDA Fortran Look Like? attributes(global) subroutine mm_kernel ( A, B, C, N, M, L ) real :: A(N,M), B(M,L), C(N,L), Cij integer, value :: N, M, L integer :: i, j, kb, k, tx, ty real, device, allocatable, dimension(:,:) :: real, shared :: Asub(16,16),Bsub(16,16) Adev,Bdev,Cdev tx = threadidx%x . ty = threadidx%y i = blockidx%x * 16 + tx allocate (Adev(N,M), Bdev(M,L), Cdev(N,L)) j = blockidx%y * 16 + ty Adev = A(1:N,1:M) Cij = 0.0 Bdev = B(1:M,1:L) do kb = 1, M, 16 Asub(tx,ty) = A(i,kb+tx-1) call mm_kernel <<<dim3(N/16,M/16),dim3(16,16)>>> Bsub(tx,ty) = B(kb+ty-1,j) ( Adev, Bdev, Cdev, N, M, L) call syncthreads() do k = 1,16 C(1:N,1:L) = Cdev Cij = Cij + Asub(tx,k) * Bsub(k,ty) deallocate ( Adev, Bdev, Cdev ) enddo call syncthreads() .
    [Show full text]
  • Opencl Actors-Adding Data Parallelism to Actor-Based Programming With
    OpenCL Actors { Adding Data Parallelism to Actor-based Programming with CAF Raphael Hiesgen∗1, Dominik Charousset1, and Thomas C. Schmidt1 1Hamburg University of Applied Sciences Internet Technologies Group Department Informatik, HAW Hamburg Berliner Tor 7, D-20099 Hamburg, Germany September 25, 2017 Abstract The actor model of computation has been designed for a seamless support of concurrency and dis- tribution. However, it remains unspecific about data parallel program flows, while available processing power of modern many core hardware such as graphics processing units (GPUs) or coprocessors increases the relevance of data parallelism for general-purpose computation. In this work, we introduce OpenCL-enabled actors to the C++ Actor Framework (CAF). This offers a high level interface for accessing any OpenCL device without leaving the actor paradigm. The new type of actor is integrated into the runtime environment of CAF and gives rise to transparent message passing in distributed systems on heterogeneous hardware. Following the actor logic in CAF, OpenCL kernels can be composed while encapsulated in C++ actors, hence operate in a multi-stage fashion on data resident at the GPU. Developers are thus enabled to build complex data parallel programs from primitives without leaving the actor paradigm, nor sacrificing performance. Our evaluations on commodity GPUs, an Nvidia TESLA, and an Intel PHI reveal the expected linear scaling behavior when offloading larger workloads. For sub-second duties, the efficiency of offloading was found to largely differ between devices. Moreover, our findings indicate a negligible overhead over programming with the native OpenCL API. Keywords: Actor Model, C++, GPGPU Computing, OpenCL, Coprocessor 1 Introduction The stagnating clock speed forced CPU manufacturers into steadily increasing the number of cores on commodity hardware to meet the ever-increasing demand for computational power.
    [Show full text]
  • PGI Compiler Reference Manual
    ® PGI Compiler Reference Manual Parallel Fortran, C and C++ for Scientists and Engineers Release 2012 The Portland Group While every precaution has been taken in the preparation of this document, The Portland Group® (PGI®), a wholly-owned subsidiary of STMicroelectronics, Inc., makes no warranty for the use of its products and assumes no responsibility for any errors that may appear, or for damages resulting from the use of the information contained herein. The Portland Group retains the right to make changes to this information at any time, without notice. The software described in this document is distributed under license from STMicroelectronics and/or The Portland Group and may be used or copied only in accordance with the terms of the end-user license agreement ("EULA"). PGI Workstation, PGI Server, PGI Accelerator, PGF95, PGF90, PGFORTRAN, and PGI Unified Binary are trademarks; and PGI, PGHPF, PGF77, PGCC, PGC++, PGI Visual Fortran, PVF, PGI CDK, Cluster Development Kit, PGPROF, PGDBG, and The Portland Group are registered trademarks of The Portland Group Incorporated. Other brands and names are property of their respective owners. No part of this document may be reproduced or transmitted in any form or by any means, for any purpose other than the purchaser's or the end user's personal use without the express written permission of STMicroelectronics and/or The Portland Group. PGI® Compiler Reference Manual Copyright © 2010-2012 STMicroelectronics, Inc. All rights reserved. Printed in the United States of America First printing:
    [Show full text]