Tools for Debugging & Profiling
|
|
|
- Beryl Freeman
- 7 years ago
- Views:
Transcription
1 Tools for Debugging & Profiling OpenACC Course 2016 Andreas Herten, Forschungszentrum Jülich, 24 October 2016
2 Contents What you will learn. Hopefully. OpenACC can greatly speedup porting to GPU But many details hidden from user Compiler makes assumptions Programmer makes mistakes Insight into program needed Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 2 20
3 Contents What you will learn. Hopefully. OpenACC can greatly speedup porting to GPU But many details hidden from user Compiler makes assumptions Programmer makes mistakes Insight into program needed Introduction PGI Tools Runtime Measurements pgprof NVIDIA Tools cuda-memcheck cuda-gdb nvprof Visual Profiler Tasks Task 1 Task 2 Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 2 20
4 Exposition $./spmv call to custreamsynchronize returned error 700: Illegal address during kernel execution Where does error come from? Is it an error at all? and how do I find out? Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 3 20
5 General notes Building for debugging -g Add debug information to executable; adds overhead program performs slower Usually, in host code, -g has little impact. -ta=tesla:lineinfo Add information to assembly to relate instructions to source code (light debug info) Check compiler output: -Minfo=accel Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 4 20
6 PGI Runtime Measurements For quick sanity checks Applications compiled with PGI compiler: Analyze via environment variables Maybe simplest/quickest check PGI_ACC_TIME Lightweight profiler for time of data movement and kernels PGI_ACC_NOTIFY Print information for GPU-related events. Set to number, to print =1 kernel launches only =2 data transfers only =3 kernel launches and data transfers =4 region entry/exits only =5 region entry/exits and kernel launches =8 wait operations, synchronizations =16 (de)allocation of device memory Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 5 20
7 PGI Runtime Measurements Usage: PGI_ACC_NOTIFY=3./app Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 6 20
8 PGPROF Graphical Performance Profiler PGI s graphical profiler Graphical, interactive profiler Comes with PGI s compiler collection Nice visualizations, quick insight For OpenACC, OpenMP, CUDA Close to NVIDIA Visual Profiler Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 7 20
9 PGPROF Graphical Performance Profiler PGI s graphical profiler Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 7 20
10 PGPROF Graphical Performance Profiler NVIDIA Visual Profiler Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 7 20
11 cuda-memcheck Command-line memory access analyzer Memory error detector; similar to Valgrind s memcheck One of most helpful tools for error-finding Out-of-bounds accesses Kernels/API execution failures Memory leaks Has sub-tools, via cuda-memcheck --tool NAME: memcheck: Memory access checking (default) racecheck: Shared memory hazard checking Also: synccheck, initcheck Remember to compile program with debug information: -g Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 8 20
12 cuda-memcheck Example Start via cuda-memcheck app Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 9 20
13 cuda-gdb Symbolic debugger Powerful symbolic debugger for CUDA code Built on top of gdb Full usage: own course needed Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 10 20
14 cuda-gdb Symbolic debugger Powerful symbolic debugger for CUDA code Built on top of gdb Full usage: own course needed cuda-gdb 101 run Starts application, give arguments with set args 1 2 break L Create breakpoint L: function name, line number LN, or FILE:LN continue Continue running print i Print content of i info locals Print all currently set variables info cuda threads Print current thread configuration cuda thread N Switch context to thread number N cheat sheet Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 10 20
15 cuda-gdb Symbolic debugger Powerful symbolic debugger for CUDA code Built on top of gdb Full usage: own course needed cuda-gdb 101 run Starts application, give arguments with set args 1 2 break L Create breakpoint L: function name, line number LN, or FILE:LN continue Continue running print i Print content of i info locals Print all currently set variables info cuda threads Print current thread configuration cuda thread N Switch context to thread number N cheat sheet Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 10 20
16 cuda-gdb With OpenACC cuda-gdb can be used for OpenACC as well! Problem: Name of OpenACC-generated kernel? Recipe: strings./app grep.*_gpu sort uniq strings./app Print occurrences of 4 printable characters grep.*_gpu Search for _gpu line endings sort uniq Eliminate duplicates from list Examples of kernel names Pattern: function_line_gpu C main_42_gpu Fortran spmv_26_gpu Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 11 20
17 cuda-gdb Example Start via cuda-gdb app run Set breakpoint with break func or break L or break file.c:l Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 12 20
18 nvprof / pgprof Command-line GPU profiler Profiles CUDA kernels and API calls; also CPU code! Suitable for OpenACC as well pgprof: Very similar to nvprof, but different default options Generate performance reports, timelines; measure events and metrics Powerful complete tool for GPU application analysis Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 13 20
19 nvprof Example Start via nvprof./app Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 14 20
20 Visual Profiler Graphical analysis Timeline view of all things GPU (API calls, kernels, memory) study stages and interplay of application View launch and run configurations Guided and unguided analysis, with (among others): Performance limiters Kernel and execution properties Memory access patterns Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 15 20
21 Visual Profiler Example Start via nvvp File New Session Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 16 20
22 Visual Profiler Example Start via nvvp File New Session Expert Analysis Timeline Selection Details Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 16 20
23 Task 1 Location of tasks: ACCOUNT/Course/Debugging/ Tasks available in C and Fortran Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 17 20
24 Task 1 Location of tasks: ACCOUNT/Course/Debugging/ Tasks available in C and Fortran Task 1: Vector addition and reduction: a = b + c γ = i c i Steps Build! make Run! srun./vecaddred.bin Fix! cuda-memcheck; cuda-gdb; Result should be 1. Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 17 20
25 Task 1 Location of tasks: ACCOUNT/Course/Debugging/ Tasks available in C and Fortran Task 1: Vector addition and reduction: a = b + c γ = i c i Steps Build! make Run! srun./vecaddred.bin Fix! cuda-memcheck; cuda-gdb; Result should be 1. JURECA Getting Started module load PGI CUDA salloc --reservation=openacc --partition=gpus --nodes=1 --time=1:30:00 --gres=mem128,gpu:4 srun cuda-memcheck./vecaddred.bin Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 17 20
26 Task 2 Task 2: Sparse Matrix-Vector Product (SpMV): x = A y Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 18 20
27 Task 2 Task 2: Sparse Matrix-Vector Product (SpMV): x = A y CSR data layout Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 18 20
28 Task 2 Task 2: Sparse Matrix-Vector Product (SpMV): x = A y CSR data layout Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 18 20
29 Task 2 Task 2: Sparse Matrix-Vector Product (SpMV): x = A y CSR data layout Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 18 20
30 Task 2 Task 2: Sparse Matrix-Vector Product (SpMV): x = A y CSR data layout val Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 18 20
31 Task 2 Task 2: Sparse Matrix-Vector Product (SpMV): x = A y CSR data layout val Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 18 20
32 Task 2 Task 2: Sparse Matrix-Vector Product (SpMV): x = A y CSR data layout col_ptr val Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 18 20
33 Task 2 Task 2: Sparse Matrix-Vector Product (SpMV): x = A y CSR data layout row_ptr col_ptr val Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 18 20
34 Task 2 Task 2: Sparse Matrix-Vector Product (SpMV): x = A y CSR data layout Build! Run! Fix! Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 19 20
35 Task 2 Task 2: Sparse Matrix-Vector Product (SpMV): x = A y CSR data layout Build! Run! Fix! JURECA Getting Started module load PGI CUDA salloc --reservation=openacc --partition=gpus --nodes=1 --time=1:30:00 --gres=mem128,gpu:4 srun cuda-memcheck./spmv.bin Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 19 20
36 Summary & Conclusion All the CUDA debugging and performance measurement tools work pgprof cuda-memcheck cuda-gdb nvprof Visual Profiler Sometimes, a little digging is needed to find automatically-generated function names Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 20 20
37 Summary & Conclusion All the CUDA debugging and performance measurement tools work pgprof cuda-memcheck cuda-gdb nvprof Visual Profiler Sometimes, a little digging is needed to find automatically-generated function names Happy Debugging! a.herten@fz-juelich.de Andreas Herten Tools for Debugging & Profiling 24 October 2016 # 20 20
CUDA Tools for Debugging and Profiling. Jiri Kraus (NVIDIA)
Mitglied der Helmholtz-Gemeinschaft CUDA Tools for Debugging and Profiling Jiri Kraus (NVIDIA) GPU Programming@Jülich Supercomputing Centre Jülich 7-9 April 2014 What you will learn How to use cuda-memcheck
GPU Tools Sandra Wienke
Sandra Wienke Center for Computing and Communication, RWTH Aachen University MATSE HPC Battle 2012/13 Rechen- und Kommunikationszentrum (RZ) Agenda IDE Eclipse Debugging (CUDA) TotalView Profiling (CUDA
ANDROID DEVELOPER TOOLS TRAINING GTC 2014. Sébastien Dominé, NVIDIA
ANDROID DEVELOPER TOOLS TRAINING GTC 2014 Sébastien Dominé, NVIDIA AGENDA NVIDIA Developer Tools Introduction Multi-core CPU tools Graphics Developer Tools Compute Developer Tools NVIDIA Developer Tools
XID ERRORS. vr352 May 2015. XID Errors
ID ERRORS vr352 May 2015 ID Errors Introduction... 1 1.1. What Is an id Message... 1 1.2. How to Use id Messages... 1 Working with id Errors... 2 2.1. Viewing id Error Messages... 2 2.2. Tools That Provide
Optimizing Application Performance with CUDA Profiling Tools
Optimizing Application Performance with CUDA Profiling Tools Why Profile? Application Code GPU Compute-Intensive Functions Rest of Sequential CPU Code CPU 100 s of cores 10,000 s of threads Great memory
NVIDIA Tools For Profiling And Monitoring. David Goodwin
NVIDIA Tools For Profiling And Monitoring David Goodwin Outline CUDA Profiling and Monitoring Libraries Tools Technologies Directions CScADS Summer 2012 Workshop on Performance Tools for Extreme Scale
TEGRA X1 DEVELOPER TOOLS SEBASTIEN DOMINE, SR. DIRECTOR SW ENGINEERING
TEGRA X1 DEVELOPER TOOLS SEBASTIEN DOMINE, SR. DIRECTOR SW ENGINEERING NVIDIA DEVELOPER TOOLS BUILD. DEBUG. PROFILE. C/C++ IDE INTEGRATION STANDALONE TOOLS HARDWARE SUPPORT CPU AND GPU DEBUGGING & PROFILING
Guided Performance Analysis with the NVIDIA Visual Profiler
Guided Performance Analysis with the NVIDIA Visual Profiler Identifying Performance Opportunities NVIDIA Nsight Eclipse Edition (nsight) NVIDIA Visual Profiler (nvvp) nvprof command-line profiler Guided
5x in 5 hours Porting SEISMIC_CPML using the PGI Accelerator Model
5x in 5 hours Porting SEISMIC_CPML using the PGI Accelerator Model C99, C++, F2003 Compilers Optimizing Vectorizing Parallelizing Graphical parallel tools PGDBG debugger PGPROF profiler Intel, AMD, NVIDIA
Building Embedded Systems
All Rights Reserved. The contents of this document cannot be reproduced without prior permission of the authors. Building Embedded Systems Chapter 5: Maintenance and Debugging Andreas Knirsch andreas.knirsch@h-da.de
Hands-on CUDA exercises
Hands-on CUDA exercises CUDA Exercises We have provided skeletons and solutions for 6 hands-on CUDA exercises In each exercise (except for #5), you have to implement the missing portions of the code Finished
NVIDIA CUDA GETTING STARTED GUIDE FOR MAC OS X
NVIDIA CUDA GETTING STARTED GUIDE FOR MAC OS X DU-05348-001_v6.5 August 2014 Installation and Verification on Mac OS X TABLE OF CONTENTS Chapter 1. Introduction...1 1.1. System Requirements... 1 1.2. About
Overview. Lecture 1: an introduction to CUDA. Hardware view. Hardware view. hardware view software view CUDA programming
Overview Lecture 1: an introduction to CUDA Mike Giles mike.giles@maths.ox.ac.uk hardware view software view Oxford University Mathematical Institute Oxford e-research Centre Lecture 1 p. 1 Lecture 1 p.
CUDA Optimization with NVIDIA Tools. Julien Demouth, NVIDIA
CUDA Optimization with NVIDIA Tools Julien Demouth, NVIDIA What Will You Learn? An iterative method to optimize your GPU code A way to conduct that method with Nvidia Tools 2 What Does the Application
OpenACC 2.0 and the PGI Accelerator Compilers
OpenACC 2.0 and the PGI Accelerator Compilers Michael Wolfe The Portland Group michael.wolfe@pgroup.com This presentation discusses the additions made to the OpenACC API in Version 2.0. I will also present
Experiences with Tools at NERSC
Experiences with Tools at NERSC Richard Gerber NERSC User Services Programming weather, climate, and earth- system models on heterogeneous mul>- core pla?orms September 7, 2011 at the Na>onal Center for
GPU Hardware and Programming Models. Jeremy Appleyard, September 2015
GPU Hardware and Programming Models Jeremy Appleyard, September 2015 A brief history of GPUs In this talk Hardware Overview Programming Models Ask questions at any point! 2 A Brief History of GPUs 3 Once
serious tools for serious apps
524028-2 Label.indd 1 serious tools for serious apps Real-Time Debugging Real-Time Linux Debugging and Analysis Tools Deterministic multi-core debugging, monitoring, tracing and scheduling Ideal for time-critical
Case Study on Productivity and Performance of GPGPUs
Case Study on Productivity and Performance of GPGPUs Sandra Wienke wienke@rz.rwth-aachen.de ZKI Arbeitskreis Supercomputing April 2012 Rechen- und Kommunikationszentrum (RZ) RWTH GPU-Cluster 56 Nvidia
How To Test Your Code On A Cuda Gdb (Gdb) On A Linux Computer With A Gbd (Gbd) And Gbbd Gbdu (Gdb) (Gdu) (Cuda
Mitglied der Helmholtz-Gemeinschaft Hands On CUDA Tools and Performance-Optimization JSC GPU Programming Course 26. März 2011 Dominic Eschweiler Outline of This Talk Introduction Setup CUDA-GDB Profiling
Testing for Security
Testing for Security Kenneth Ingham September 29, 2009 1 Course overview The threat that security breaches present to your products and ultimately your customer base can be significant. This course is
HPC Wales Skills Academy Course Catalogue 2015
HPC Wales Skills Academy Course Catalogue 2015 Overview The HPC Wales Skills Academy provides a variety of courses and workshops aimed at building skills in High Performance Computing (HPC). Our courses
Profiler User's Guide
Version 2016 www.pgroup.com TABLE OF CONTENTS Profiling Overview... iv What's New... iv Terminology... v Chapter 1. Preparing An Application For Profiling...1 1.1. Focused Profiling...1 1.2. Marking Regions
NVIDIA CUDA GETTING STARTED GUIDE FOR MAC OS X
NVIDIA CUDA GETTING STARTED GUIDE FOR MAC OS X DU-05348-001_v5.5 July 2013 Installation and Verification on Mac OS X TABLE OF CONTENTS Chapter 1. Introduction...1 1.1. System Requirements... 1 1.2. About
Debugging in Heterogeneous Environments with TotalView. ECMWF HPC Workshop 30 th October 2014
Debugging in Heterogeneous Environments with TotalView ECMWF HPC Workshop 30 th October 2014 Agenda Introduction Challenges TotalView overview Advanced features Current work and future plans 2014 Rogue
Introduction to GPU Programming Languages
CSC 391/691: GPU Programming Fall 2011 Introduction to GPU Programming Languages Copyright 2011 Samuel S. Cho http://www.umiacs.umd.edu/ research/gpu/facilities.html Maryland CPU/GPU Cluster Infrastructure
Debugging CUDA Applications Przetwarzanie Równoległe CUDA/CELL
Debugging CUDA Applications Przetwarzanie Równoległe CUDA/CELL Michał Wójcik, Tomasz Boiński Katedra Architektury Systemów Komputerowych Wydział Elektroniki, Telekomunikacji i Informatyki Politechnika
CUDA Debugging. GPGPU Workshop, August 2012. Sandra Wienke Center for Computing and Communication, RWTH Aachen University
CUDA Debugging GPGPU Workshop, August 2012 Sandra Wienke Center for Computing and Communication, RWTH Aachen University Nikolay Piskun, Chris Gottbrath Rogue Wave Software Rechen- und Kommunikationszentrum
OpenACC Basics Directive-based GPGPU Programming
OpenACC Basics Directive-based GPGPU Programming Sandra Wienke, M.Sc. wienke@rz.rwth-aachen.de Center for Computing and Communication RWTH Aachen University Rechen- und Kommunikationszentrum (RZ) PPCES,
Introducing PgOpenCL A New PostgreSQL Procedural Language Unlocking the Power of the GPU! By Tim Child
Introducing A New PostgreSQL Procedural Language Unlocking the Power of the GPU! By Tim Child Bio Tim Child 35 years experience of software development Formerly VP Oracle Corporation VP BEA Systems Inc.
Programming models for heterogeneous computing. Manuel Ujaldón Nvidia CUDA Fellow and A/Prof. Computer Architecture Department University of Malaga
Programming models for heterogeneous computing Manuel Ujaldón Nvidia CUDA Fellow and A/Prof. Computer Architecture Department University of Malaga Talk outline [30 slides] 1. Introduction [5 slides] 2.
The Top Six Advantages of CUDA-Ready Clusters. Ian Lumb Bright Evangelist
The Top Six Advantages of CUDA-Ready Clusters Ian Lumb Bright Evangelist GTC Express Webinar January 21, 2015 We scientists are time-constrained, said Dr. Yamanaka. Our priority is our research, not managing
Introduction to Linux and Cluster Basics for the CCR General Computing Cluster
Introduction to Linux and Cluster Basics for the CCR General Computing Cluster Cynthia Cornelius Center for Computational Research University at Buffalo, SUNY 701 Ellicott St Buffalo, NY 14203 Phone: 716-881-8959
Developing Parallel Applications with the Eclipse Parallel Tools Platform
Developing Parallel Applications with the Eclipse Parallel Tools Platform Greg Watson IBM STG grw@us.ibm.com Parallel Tools Platform Enabling Parallel Application Development Best practice tools for experienced
OpenACC Programming and Best Practices Guide
OpenACC Programming and Best Practices Guide June 2015 2015 openacc-standard.org. All Rights Reserved. Contents 1 Introduction 3 Writing Portable Code........................................... 3 What
ultra fast SOM using CUDA
ultra fast SOM using CUDA SOM (Self-Organizing Map) is one of the most popular artificial neural network algorithms in the unsupervised learning category. Sijo Mathew Preetha Joy Sibi Rajendra Manoj A
Integrating SNiFF+ with the Data Display Debugger (DDD)
1.1 1 of 5 Integrating SNiFF+ with the Data Display Debugger (DDD) 1. Introduction In this paper we will describe the integration of SNiFF+ with the Data Display Debugger (DDD). First we will start with
Software Engineering Best Practices. Christian Hartshorne Field Engineer Daniel Thomas Internal Sales Engineer
Software Engineering Best Practices Christian Hartshorne Field Engineer Daniel Thomas Internal Sales Engineer 2 3 4 Examples of Software Engineering Debt (just some of the most common LabVIEW development
Basics of VTune Performance Analyzer. Intel Software College. Objectives. VTune Performance Analyzer. Agenda
Objectives At the completion of this module, you will be able to: Understand the intended purpose and usage models supported by the VTune Performance Analyzer. Identify hotspots by drilling down through
E6895 Advanced Big Data Analytics Lecture 14:! NVIDIA GPU Examples and GPU on ios devices
E6895 Advanced Big Data Analytics Lecture 14: NVIDIA GPU Examples and GPU on ios devices Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science IBM Chief Scientist,
RWTH GPU Cluster. Sandra Wienke wienke@rz.rwth-aachen.de November 2012. Rechen- und Kommunikationszentrum (RZ) Fotos: Christian Iwainsky
RWTH GPU Cluster Fotos: Christian Iwainsky Sandra Wienke wienke@rz.rwth-aachen.de November 2012 Rechen- und Kommunikationszentrum (RZ) The RWTH GPU Cluster GPU Cluster: 57 Nvidia Quadro 6000 (Fermi) innovative
The Asterope compute cluster
The Asterope compute cluster ÅA has a small cluster named asterope.abo.fi with 8 compute nodes Each node has 2 Intel Xeon X5650 processors (6-core) with a total of 24 GB RAM 2 NVIDIA Tesla M2050 GPGPU
Example of Standard API
16 Example of Standard API System Call Implementation Typically, a number associated with each system call System call interface maintains a table indexed according to these numbers The system call interface
Debugging with TotalView
Tim Cramer 17.03.2015 IT Center der RWTH Aachen University Why to use a Debugger? If your program goes haywire, you may... ( wand (... buy a magic... read the source code again and again and...... enrich
GPU Performance Analysis and Optimisation
GPU Performance Analysis and Optimisation Thomas Bradley, NVIDIA Corporation Outline What limits performance? Analysing performance: GPU profiling Exposing sufficient parallelism Optimising for Kepler
How To Develop For A Powergen 2.2 (Tegra) With Nsight) And Gbd (Gbd) On A Quadriplegic (Powergen) Powergen 4.2.2 Powergen 3
Profiling and Debugging Tools for High-performance Android Applications Stephen Jones, Product Line Manager, NVIDIA (sjones@nvidia.com) Android By The Numbers 1.3M Android activations per day Android activations
GPU Profiling with AMD CodeXL
GPU Profiling with AMD CodeXL Software Profiling Course Hannes Würfel OUTLINE 1. Motivation 2. GPU Recap 3. OpenCL 4. CodeXL Overview 5. CodeXL Internals 6. CodeXL Profiling 7. CodeXL Debugging 8. Sources
NVIDIA CUDA GETTING STARTED GUIDE FOR MICROSOFT WINDOWS
NVIDIA CUDA GETTING STARTED GUIDE FOR MICROSOFT WINDOWS DU-05349-001_v6.0 February 2014 Installation and Verification on TABLE OF CONTENTS Chapter 1. Introduction...1 1.1. System Requirements... 1 1.2.
HIGH PERFORMANCE CONSULTING COURSE OFFERINGS
Performance 1(6) HIGH PERFORMANCE CONSULTING COURSE OFFERINGS LEARN TO TAKE ADVANTAGE OF POWERFUL GPU BASED ACCELERATOR TECHNOLOGY TODAY 2006 2013 Nvidia GPUs Intel CPUs CONTENTS Acronyms and Terminology...
High Performance Cloud: a MapReduce and GPGPU Based Hybrid Approach
High Performance Cloud: a MapReduce and GPGPU Based Hybrid Approach Beniamino Di Martino, Antonio Esposito and Andrea Barbato Department of Industrial and Information Engineering Second University of Naples
SKP16C62P Tutorial 1 Software Development Process using HEW. Renesas Technology America Inc.
SKP16C62P Tutorial 1 Software Development Process using HEW Renesas Technology America Inc. 1 Overview The following tutorial is a brief introduction on how to develop and debug programs using HEW (Highperformance
Optimization tools. 1) Improving Overall I/O
Optimization tools After your code is compiled, debugged, and capable of running to completion or planned termination, you can begin looking for ways in which to improve execution speed. In general, the
Part I Courses Syllabus
Part I Courses Syllabus This document provides detailed information about the basic courses of the MHPC first part activities. The list of courses is the following 1.1 Scientific Programming Environment
CUDA programming on NVIDIA GPUs
p. 1/21 on NVIDIA GPUs Mike Giles mike.giles@maths.ox.ac.uk Oxford University Mathematical Institute Oxford-Man Institute for Quantitative Finance Oxford eresearch Centre p. 2/21 Overview hardware view
Get the Better of Memory Leaks with Valgrind Whitepaper
WHITE PAPER Get the Better of Memory Leaks with Valgrind Whitepaper Memory leaks can cause problems and bugs in software which can be hard to detect. In this article we will discuss techniques and tools
RTOS Debugger for ecos
RTOS Debugger for ecos TRACE32 Online Help TRACE32 Directory TRACE32 Index TRACE32 Documents... RTOS Debugger... RTOS Debugger for ecos... 1 Overview... 2 Brief Overview of Documents for New Users... 3
Applications to Computational Financial and GPU Computing. May 16th. Dr. Daniel Egloff +41 44 520 01 17 +41 79 430 03 61
F# Applications to Computational Financial and GPU Computing May 16th Dr. Daniel Egloff +41 44 520 01 17 +41 79 430 03 61 Today! Why care about F#? Just another fashion?! Three success stories! How Alea.cuBase
High Performance Computing in Aachen
High Performance Computing in Aachen Christian Iwainsky iwainsky@rz.rwth-aachen.de Center for Computing and Communication RWTH Aachen University Produktivitätstools unter Linux Sep 16, RWTH Aachen University
Getting Started with Tizen SDK : How to develop a Web app. Hong Gyungpyo 洪 競 杓 Samsung Electronics Co., Ltd
Getting Started with Tizen SDK : How to develop a Web app Hong Gyungpyo 洪 競 杓 Samsung Electronics Co., Ltd Introduction to Tizen SDK Tizen Platform (1/2) Application Web Applications Native Applications
Visualization Tool for GPGPU Programming
ASEE 2014 Zone I Conference, April 3-5, 2014, University of Bridgeport, Bridgeport, CT, USA. Visualization Tool for GPGPU Programming Peter J. Zeno Department of Computer Science and Engineering University
Multicore Parallel Computing with OpenMP
Multicore Parallel Computing with OpenMP Tan Chee Chiang (SVU/Academic Computing, Computer Centre) 1. OpenMP Programming The death of OpenMP was anticipated when cluster systems rapidly replaced large
STLinux Software development environment
STLinux Software development environment Development environment The STLinux Development Environment is a comprehensive set of tools and packages for developing Linux-based applications on ST s consumer
Parallel Debugging with DDT
Parallel Debugging with DDT Nate Woody 3/10/2009 www.cac.cornell.edu 1 Debugging Debugging is a methodical process of finding and reducing the number of bugs, or defects, in a computer program or a piece
Why Threads Are A Bad Idea (for most purposes)
Why Threads Are A Bad Idea (for most purposes) John Ousterhout Sun Microsystems Laboratories john.ousterhout@eng.sun.com http://www.sunlabs.com/~ouster Introduction Threads: Grew up in OS world (processes).
Evaluation of CUDA Fortran for the CFD code Strukti
Evaluation of CUDA Fortran for the CFD code Strukti Practical term report from Stephan Soller High performance computing center Stuttgart 1 Stuttgart Media University 2 High performance computing center
Lecture 1: an introduction to CUDA
Lecture 1: an introduction to CUDA Mike Giles mike.giles@maths.ox.ac.uk Oxford University Mathematical Institute Oxford e-research Centre Lecture 1 p. 1 Overview hardware view software view CUDA programming
Monitoring, Tracing, Debugging (Under Construction)
Monitoring, Tracing, Debugging (Under Construction) I was already tempted to drop this topic from my lecture on operating systems when I found Stephan Siemen's article "Top Speed" in Linux World 10/2003.
GPU System Architecture. Alan Gray EPCC The University of Edinburgh
GPU System Architecture EPCC The University of Edinburgh Outline Why do we want/need accelerators such as GPUs? GPU-CPU comparison Architectural reasons for GPU performance advantages GPU accelerated systems
A Pattern-Based Comparison of OpenACC & OpenMP for Accelerators
A Pattern-Based Comparison of OpenACC & OpenMP for Accelerators Sandra Wienke 1,2, Christian Terboven 1,2, James C. Beyer 3, Matthias S. Müller 1,2 1 IT Center, RWTH Aachen University 2 JARA-HPC, Aachen
Valgrind BoF Ideas, new features and directions
Valgrind BoF Ideas, new features and directions Everybody! Valgrind developers and users are encouraged to participate by joining the discussion. And of course by kindly (or bitterly:) complain about bugs
CS3600 SYSTEMS AND NETWORKS
CS3600 SYSTEMS AND NETWORKS NORTHEASTERN UNIVERSITY Lecture 2: Operating System Structures Prof. Alan Mislove (amislove@ccs.neu.edu) Operating System Services Operating systems provide an environment for
Introduction to Embedded Systems. Software Update Problem
Introduction to Embedded Systems CS/ECE 6780/5780 Al Davis logistics minor Today s topics: more software development issues 1 CS 5780 Software Update Problem Lab machines work let us know if they don t
Chapter 3: Operating-System Structures. System Components Operating System Services System Calls System Programs System Structure Virtual Machines
Chapter 3: Operating-System Structures System Components Operating System Services System Calls System Programs System Structure Virtual Machines Operating System Concepts 3.1 Common System Components
Parallel Programming Survey
Christian Terboven 02.09.2014 / Aachen, Germany Stand: 26.08.2014 Version 2.3 IT Center der RWTH Aachen University Agenda Overview: Processor Microarchitecture Shared-Memory
Performance Analysis for GPU Accelerated Applications
Center for Information Services and High Performance Computing (ZIH) Performance Analysis for GPU Accelerated Applications Working Together for more Insight Willersbau, Room A218 Tel. +49 351-463 - 39871
SLURM: Resource Management and Job Scheduling Software. Advanced Computing Center for Research and Education www.accre.vanderbilt.
SLURM: Resource Management and Job Scheduling Software Advanced Computing Center for Research and Education www.accre.vanderbilt.edu Simple Linux Utility for Resource Management But it s also a job scheduler!
Introduction to Running Computations on the High Performance Clusters at the Center for Computational Research
! Introduction to Running Computations on the High Performance Clusters at the Center for Computational Research! Cynthia Cornelius! Center for Computational Research University at Buffalo, SUNY! cdc at
SLURM Workload Manager
SLURM Workload Manager What is SLURM? SLURM (Simple Linux Utility for Resource Management) is the native scheduler software that runs on ASTI's HPC cluster. Free and open-source job scheduler for the Linux
GPUs for Scientific Computing
GPUs for Scientific Computing p. 1/16 GPUs for Scientific Computing Mike Giles mike.giles@maths.ox.ac.uk Oxford-Man Institute of Quantitative Finance Oxford University Mathematical Institute Oxford e-research
Parallel Programming for Multi-Core, Distributed Systems, and GPUs Exercises
Parallel Programming for Multi-Core, Distributed Systems, and GPUs Exercises Pierre-Yves Taunay Research Computing and Cyberinfrastructure 224A Computer Building The Pennsylvania State University University
Chapter 3: Operating-System Structures. Common System Components
Chapter 3: Operating-System Structures System Components Operating System Services System Calls System Programs System Structure Virtual Machines System Design and Implementation System Generation 3.1
Introduction to GPU hardware and to CUDA
Introduction to GPU hardware and to CUDA Philip Blakely Laboratory for Scientific Computing, University of Cambridge Philip Blakely (LSC) GPU introduction 1 / 37 Course outline Introduction to GPU hardware
NVIDIA CUDA Software and GPU Parallel Computing Architecture. David B. Kirk, Chief Scientist
NVIDIA CUDA Software and GPU Parallel Computing Architecture David B. Kirk, Chief Scientist Outline Applications of GPU Computing CUDA Programming Model Overview Programming in CUDA The Basics How to Get
Eliminate Memory Errors and Improve Program Stability
Eliminate Memory Errors and Improve Program Stability with Intel Parallel Studio XE Can running one simple tool make a difference? Yes, in many cases. You can find errors that cause complex, intermittent
INTEL PARALLEL STUDIO XE EVALUATION GUIDE
Introduction This guide will illustrate how you use Intel Parallel Studio XE to find the hotspots (areas that are taking a lot of time) in your application and then recompiling those parts to improve overall
The OpenACC Application Programming Interface
The OpenACC Application Programming Interface Version 1.0 November, 2011 Contents 1. Introduction... 4 1.1 Scope... 4 1.2 Execution Model... 4 1.3 Memory Model... 5 1.4 Organization of this document...
Overview on Modern Accelerators and Programming Paradigms Ivan Giro7o igiro7o@ictp.it
Overview on Modern Accelerators and Programming Paradigms Ivan Giro7o igiro7o@ictp.it Informa(on & Communica(on Technology Sec(on (ICTS) Interna(onal Centre for Theore(cal Physics (ICTP) Mul(ple Socket
1. If we need to use each thread to calculate one output element of a vector addition, what would
Quiz questions Lecture 2: 1. If we need to use each thread to calculate one output element of a vector addition, what would be the expression for mapping the thread/block indices to data index: (A) i=threadidx.x
MAQAO Performance Analysis and Optimization Tool
MAQAO Performance Analysis and Optimization Tool Andres S. CHARIF-RUBIAL andres.charif@uvsq.fr Performance Evaluation Team, University of Versailles S-Q-Y http://www.maqao.org VI-HPS 18 th Grenoble 18/22
Kernel Types System Calls. Operating Systems. Autumn 2013 CS4023
Operating Systems Autumn 2013 Outline 1 2 Types of 2.4, SGG The OS Kernel The kernel is the central component of an OS It has complete control over everything that occurs in the system Kernel overview
Embedded Software development Process and Tools:
Embedded Software development Process and Tools: Lesson-2 Integrated Development Environment (IDE) 1 1. IDE 2 Consists of Simulators editors, compilers, assemblers, etc., IDE 3 emulators logic analyzers
Getting Started with CodeXL
AMD Developer Tools Team Advanced Micro Devices, Inc. Table of Contents Introduction... 2 Install CodeXL... 2 Validate CodeXL installation... 3 CodeXL help... 5 Run the Teapot Sample project... 5 Basic
Valgrind Documentation
Valgrind Documentation Release 3.10.0 10 September 2014 Copyright 2000-2014 AUTHORS Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License,
Petascale Software Challenges. William Gropp www.cs.illinois.edu/~wgropp
Petascale Software Challenges William Gropp www.cs.illinois.edu/~wgropp Petascale Software Challenges Why should you care? What are they? Which are different from non-petascale? What has changed since
Introduction to CUDA C
Introduction to CUDA C What is CUDA? CUDA Architecture Expose general-purpose GPU computing as first-class capability Retain traditional DirectX/OpenGL graphics performance CUDA C Based on industry-standard
SLURM: Resource Management and Job Scheduling Software. Advanced Computing Center for Research and Education www.accre.vanderbilt.
SLURM: Resource Management and Job Scheduling Software Advanced Computing Center for Research and Education www.accre.vanderbilt.edu Simple Linux Utility for Resource Management But it s also a job scheduler!
Compute Cluster Server Lab 3: Debugging the parallel MPI programs in Microsoft Visual Studio 2005
Compute Cluster Server Lab 3: Debugging the parallel MPI programs in Microsoft Visual Studio 2005 Compute Cluster Server Lab 3: Debugging the parallel MPI programs in Microsoft Visual Studio 2005... 1
Using. SDR-Console by Simon Brown
Using SDR-Console by Simon Brown Quick Start Guide & Hints & Tips Welcome, this is a quick start guide that will be a constantly upgraded document as changes are made to the SDRPlay RSP and to SDR-Console.
A QUICK OVERVIEW OF THE OMNeT++ IDE
Introduction A QUICK OVERVIEW OF THE OMNeT++ IDE The OMNeT++ 4.x Integrated Development Environment is based on the Eclipse platform, and extends it with new editors, views, wizards, and additional functionality.