Parallel Vector & Signal Processing

Slides:

Advertisements

Similar presentations

Statistical Modeling of Feedback Data in an Automatic Tuning System Richard Vuduc, James Demmel (U.C. Berkeley, EECS) Jeff.

Advertisements

P1801 PAR Extension Motivation Address deferred issues Consider further UPF/CPF convergence SAIF integration and extension Continue to raise the abstraction.

Now it is time to get back to work…….. Let’s see what you remember You’ll need to write down your answers…..

MPI version of the Serial Code With One-Dimensional Decomposition Presented by Timothy H. Kaiser, Ph.D. San Diego Supercomputer Center Presented by Timothy.

Addressing the Challenges of Current Software. Questions to Address Why? What? Where? How?

A PLFS Plugin for HDF5 for Improved I/O Performance and Analysis Kshitij Mehta 1, John Bent 2, Aaron Torres 3, Gary Grider 3, Edgar Gabriel 1 1 University.

GridRPC Sources / Credits: IRISA/IFSIC IRISA/INRIA Thierry Priol et. al papers.

Tuesday, September 12, 2006 Nothing is impossible for people who don't have to do it themselves. - Weiler.

CEG 221 Lesson 5: Algorithm Development II Mr. David Lippa.

Java for High Performance Computing Jordi Garcia Almiñana 14 de Octubre de 1998 de la era post-internet.

Tile Reduction: the first step towards tile aware parallelization in OpenMP Ge Gan Department of Electrical and Computer Engineering Univ. of Delaware.

Lecture 29 Fall 2006 Lecture 29: Parallel Programming Overview.

CS 591x – Cluster Computing and Programming Parallel Computers Parallel Libraries.

ICOM 5995: Performance Instrumentation and Visualization for High Performance Computer Systems Lecture 7 October 16, 2002 Nayda G. Santiago.

POOMA 2.4 Progress and Plans Scott Haney, Mark Mitchell, James Crotinger, Jeffrey Oldham, and Stephen Smith October 22, 2001 Los Alamos National Laboratory.

MIT Lincoln Laboratory hch-1 HCH 5/26/2016 Achieving Portable Task and Data Parallelism on Parallel Signal Processing Architectures Hank Hoffmann.

CSE 260 – Parallel Processing UCSD Fall 2006 A Performance Characterization of UPC Presented by – Anup Tapadia Fallon Chen.

CSC 230: C and Software Tools Rudra Dutta Computer Science Department Course Introduction.

1 The Message-Passing Model l A process is (traditionally) a program counter and address space. l Processes may have multiple threads (program counters.

Cousins HPEC 2002 Session 4: Emerging High Performance Software David Cousins Division Scientist High Performance Computing Dept. Newport,

U NIVERSITY OF M ASSACHUSETTS, A MHERST Department of Computer Science Fortress John Burgess and Richard Chang CS691W University of Massachusetts Amherst.

A Multi-platform Co-array Fortran Compiler for High-Performance Computing John Mellor-Crummey, Yuri Dotsenko, Cristian Coarfa {johnmc, dotsenko,

1 Qualifying ExamWei Chen Unified Parallel C (UPC) and the Berkeley UPC Compiler Wei Chen the Berkeley UPC Group 3/11/07.

Fortress Aaron Becker Abhinav Bhatele Hassan Jafri 2 May 2006.

JL MONGE - Chimere software evolution 1 Chimere software evolution How to make a code : –Obscure –Less efficient –Usable by nobody but its author.

1 Parallel Vector & Signal Processing Mark Mitchell CodeSourcery, LLC April 3, 2003.

08/10/ NRL Hybrid QR Factorization Algorithm for High Performance Computing Architectures Peter Vouras Naval Research Laboratory Radar Division Professor.

Implementation of a Shipboard Ballistic Missile Defense Processing Application Using the High Performance Embedded Computing Software Initiative (HPEC-SI)

Generic Compressed Matrix Insertion P ETER G OTTSCHLING – S MART S OFT /TUD D AG L INDBO – K UNGLIGA T EKNISKA H ÖGSKOLAN SmartSoft – TU Dresden

A Multi-platform Co-Array Fortran Compiler for High-Performance Computing Cristian Coarfa, Yuri Dotsenko, John Mellor-Crummey {dotsenko, ccristi,

1 VSIPL++: Parallel Performance HPEC 2004 CodeSourcery, LLC September 30, 2004.

C OMPUTATIONAL R ESEARCH D IVISION 1 Defining Software Requirements for Scientific Computing Phillip Colella Applied Numerical Algorithms Group Lawrence.

1 HPJAVA I.K.UJJWAL 07M11A1217 Dept. of Information Technology B.S.I.T.

High Performance Flexible DSP Infrastructure Based on MPI and VSIPL 7th Annual Workshop on High Performance Embedded Computing MIT Lincoln Laboratory

GPU VSIPL: Core and Beyond Andrew Kerr 1, Dan Campbell 2, and Mark Richards 1 1 Georgia Institute of Technology 2 Georgia Tech Research Institute.

Parallel Programming & Cluster Computing Linear Algebra Henry Neeman, University of Oklahoma Paul Gray, University of Northern Iowa SC08 Education Program’s.

Corflow Online Tutorial Eric Chung

PERFORMANCE OF THE OPENMP AND MPI IMPLEMENTATIONS ON ULTRASPARC SYSTEM Abstract Programmers and developers interested in utilizing parallel programming.

Quantum Shift Register Circuits Mark M. Wilde arXiv: National Institute of Standards and Technology, Wednesday, June 10, 2009 To appear in Physical.

Group Members Hamza Zahid (131391) Fahad Nadeem khan Abdual Hannan AIR UNIVERSITY MULTAN CAMPUS.

Optimizing the Performance of Sparse Matrix-Vector Multiplication

Verification of Data-Dependent Properties of MPI-Based Parallel Scientific Software Anastasia Mironova.

High Performance Embedded Computing Software Initiative (HPEC-SI)

Miraj Kheni Authors: Toyotaro Suzumura, Koji Ueno

An Emerging, Portable Co-Array Fortran Compiler for High-Performance Computing Daniel Chavarría-Miranda, Cristian Coarfa, Yuri.

Game Architecture Rabin is a good overview of everything to do with Games A lot of these slides come from the 1st edition CS 4455.

M. Richards1 ,D. Campbell1 (presenter), R. Judd2, J. Lebak3, and R

SysML v2 Formalism: Requirements & Benefits

GdX - Grid eXplorer parXXL: A Fine Grained Development Environment on Coarse Grained Architectures PARA 2006 – UMEǺ Jens Gustedt - Stéphane Vialle - Amelia.

Use algebra to construct proofs.

GPU VSIPL: High Performance VSIPL Implementation for GPUs

Sparse Matrix-Vector Multiplication (Sparsity, Bebop)

Compiling Dynamic Data Structures in Python to Enable the Use of Multi-core and Many-core Libraries Bin Ren, Gagan Agrawal 9/18/2018.

Vector Processing => Multimedia

Sourcery VSIPL++ for Cell/B.E.

Many-core Software Development Platforms

C Passing arrays to a Function

Distributed Systems CS

Adaptive Strassen and ATLAS’s DGEMM

SPAWAR Systems Center San Diego

MPI-Message Passing Interface

Compiler Back End Panel

STUDY AND IMPLEMENTATION

X-STANDARD MATHEMATICS ONE MARK QUESTIONS

Compiler Back End Panel

Numerical Algorithms Quiz questions

Baremetal C Programming for Embedded Systems

EE 4xx: Computer Architecture and Performance Programming

VSIPL++: Parallel Performance HPEC 2004

FOUNDATIONS OF MODERN SCIENTIFIC PROGRAMMING

Presentation transcript:

Parallel Vector & Signal Processing CodeSourcery, LLC September 26, 2002

Overview VSIPL++ is a C++ library for: Vector processing Signal processing Applications: Missile systems Radar arrays

Objectives Performance Parallelism Portability Rapid Development Proven techniques from POOMA Parallelism SPMD Portability Standard ISO C++, MPI Rapid Development High-level abstractions

Development Plan Specification Development Reference Implementation Substantially complete Reference Implementation Serial implementation at proof-of-concept stage Parallel implementation in FY 2003 Optimized Vendor Implementations Not yet underway

Background: VSIPL C library for vector and signal-processing applications Multiple vendor implementations VSIPL++ Additions: Parallelism Simpler syntax Extensibility VSIPL and its goals will be described by Mark Richards (GTRI) in the previous talk.

C  a  A  conjug(A)t + b  C BLAS zherk Routine zherk performs a rank-k update of Hermitian matrix C: C  a  A  conjug(A)t + b  C Matrix<complex<double> > A(10,15); Matrix<complex<double> > C(10,10); C = alpha * prodh(A,A) + beta * C; // Matrices and data automatically destroyed.

Development Benefits Automatic parallelism No explicit MPI calls Code uses mathematical notation Automatic memory allocation Fewer opportunities for error. Reduced development costs Reduced development time

Contact Information Mark Mitchell mark@codesourcery.com Jeffrey Oldham oldham@codesourcery.com

Parallel Vector & Signal Processing CodeSourcery, LLC September 26, 2002