Docsity
Docsity

Prepare for your exams
Prepare for your exams

Study with the several resources on Docsity


Earn points to download
Earn points to download

Earn points by helping other students or get them with a premium plan


Guidelines and tips
Guidelines and tips

Revisiting the Sequential Programming Model for Multi-Core | CISC 879, Exams of Computer Science

Material Type: Exam; Class: ADVANCED PARALLEL PROGRAMMING; Subject: Computer/Information Sciences; University: University of Delaware; Term: Unknown 1989;

Typology: Exams

Pre 2010

Uploaded on 09/02/2009

koofers-user-bya
koofers-user-bya 🇺🇸

10 documents

1 / 27

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
CISC 879 : Software Support for Multicore Architectures
Presenter: Kishen Maloor
Dept of Computer & Information Sciences
University of Delaware
Revisiting the Sequential
Programming Model for Multi-Core
Matthew J. Bridges Neil Vachharajani Yun Zhang Thomas
Jablin David I. August
Department of Computer Science
Princeton University
pf3
pf4
pf5
pf8
pf9
pfa
pfd
pfe
pff
pf12
pf13
pf14
pf15
pf16
pf17
pf18
pf19
pf1a
pf1b

Partial preview of the text

Download Revisiting the Sequential Programming Model for Multi-Core | CISC 879 and more Exams Computer Science in PDF only on Docsity!

Presenter: Kishen Maloor Dept of Computer & Information Sciences University of Delaware

Revisiting the Sequential

Programming Model for Multi-Core

Matthew J. Bridges Neil Vachharajani Yun Zhang Thomas Jablin David I. August Department of Computer Science Princeton University

Motivation

  • Move to multi-threaded programming is costly.
    • Parallel programming models: costly to adopt.
  • Need for automatic parallelization.
    • Large number of existing single-threaded applications.
  • Past attempts have been Insufficient to keep many cores busy.

Parallelization Framework

  • Compiler and hardware support.
    • Thread level speculation.
      • Execute loop iterations in parallel.
      • Needs to buffer results.
    • Decoupled software pipelining.
      • Partition loop into stages; execute in parallel.

Parallelization Framework

  • Attempt to extract DOALL parallelism.
  • Use of alias and value speculation.
  • Avoid misspeculation.
    • Synchronizing some dependences.
    • Forwarding stored values to later threads.

Y-branch

dict = start_dictionary(); while ((char = read(1)) != EOF) { profitable = compress(char, dict) @YBRANCH(probability=.00001) if (!profitable) dict = restart_dictionary(dict); } finish_dictionary(dict); Use of a y-branch #define CUTOFF 100000 dict = start_dictionary(); int count = 0; while ((char = read(1)) != EOF) { profitable = compress(char, dict) if (!profitable) { dict = restart_dictionary(dict); } else if (count == CUTOFF) { dict = restart_dictionary(dict); count = 0; } count++; } finish_dictionary(dict);

Commutative

static int seed; @Commutative int Yacm_random() { int temp = seed / 127773L; seed = 16807L * (seed - temp * 127773L) - (temp * 2836L); if( seed < 0 ) seed += 2147483647L; Return seed; } Use of commutative

Experimentation Approach

Experimentation Approach

  • Extension of DSWP.
    • Uses speculation
  • Phases are statically selected “regions” in code.
  • “Tasks” are dynamic instances of these regions.

Case Studies

  • Manual parallelization of the SPEC CINT2000 benchmarks.
  • Uses the described experimentation approach.
  • Demonstrate use of known compiler technologies.
  • Experiments performed with 1- cores.

256.bzip

  • Compresses and decompresses a file.
  • Input file is divided into independent blocks of same size.
  • Use DSWP parallelization.
    • Phase A thread reads in blocks.
    • Phase B threads compress blocks, buffer results.
    • Phase C threads write result to output stream.

253.perlbmk

  • Interpreter for the Perl language.
  • Source statements => set of operations demarcated by NEXTSTATE operations.
  • Executed using a virtual stack machine.
  • Compiler can precompute next NEXTSTATE operation.
  • Execute sets of operations representing perl statements in parallel.

181.mcf

  • Solves a combinatorial optimization problem using a network simplex algorithm.
  • Main loop of this algorithm parallelized using value speculation.

254.gap

  • Interpreter for a computational discrete algebra programming language.
  • Speculate that statements are data independent.
  • Memory allocation routines marked commutative.
  • Misspeculation results:
    • Due to true data dependences.
  • Due the garbage collection performed.

186.crafty

  • Application that plays chess.
  • Uses a recursive search function.
  • Can search each of the moves in the root list of moves independently.
  • Uses caches to some ways to prune the search space and improve performance.
  • Cache lookup function marked as commutative.
  • Unroll recursion and parallelize.