The Implementation of Cilk-5 Multithreaded Language
|
|
- Sybil Mitchell
- 5 years ago
- Views:
Transcription
1 The Implementation of Cilk-5 Multithreaded Language By Matteo Frigo, Charles E. Leiserson, and Keith H Randall Presented by Martin Skou 1/14
2 The authors Matteo Frigo Chief Scientist and founder of Cilk Arts Ph.D. in 1999 from the Department of Electrical Engineering and Computer Science at MIT. Awards George M. Sprowls award for outstanding doctoral dissertations in computer science at MIT Charles E. Leiserson Chairman, Chief Technology Officer and Founder of Cilk Arts Professor of Computer Science and Engineering at MIT MacVicar Faculty Fellow at MIT ACM Fellow Keith H. Randall Software engineer at Google Ph.d from MIT 2/14
3 The Paper ACM SIGPLAN Notices 1998 Association for Computing Machinery's Special Interest Group on programming languages Most Influential PLDI Paper Award 2008 A paper presented at the PLDI held 10 years prior to the award year. Includes a prize of $1,000 Programming Language Design and Implementation (PLDI) One of the ACM SIGPLAN's most important conferences. 3/14
4 Introduction to Cilk Language for multithreaded parallel programming based on C. Faithful extension of C It can on one processor scales down to run nearly as fast as the serial version of C, called C elision Designed for general-purpose parallel programming Has a scheduler that allows the performance of programs to be estimated Developed by the MIT CSAIL Supercomputing Technologies Group under the leadership of prof. Charles Leiserson. in 1994 Funding Supported in part by NSF Previous support in part by DARPA Latest version is Designed on the work-first principle Reduce the work overhead 4/14
5 Introduction to Cilk Performance of a Cilk computation work total execution time in serial critical-path length execution time on a infinite number of processors Uses a workstealing scheduler In Cilk-1: scheduler optimized only at compile time in Cilk-5: scheduler optimize both at compile time and run time 5/14
6 The Cilk language Uses keywords cilk identifies a function which is written in Cilk spawn Indicates that the procedure call it modifies can safely operate in parallel with other executing code sync Indicates that execution of the current procedure cannot proceed until all previously spawned procedures have completed and returned their results to the parent frame inlet Identifies a function defined within the procedure as an inlet abort Can only be used inside an inlet Tells the scheduler that any other procedures that have been spawned off by the parent procedure can safely be aborted 6/14
7 Example in Cilk #include tstdlib.h> #include <stdio.h> #include <cilk.h> cilk int fib (int n) { if (n(2) return n; else { int x, y; x = spawn fib (n-1); y = spawn fib (n-2); sync; return (x+y) ; } cilk int main (int argc, char *argv[]) { int n. result; n = atoi(argv[1]); result. = spawn fib(n); sync ; printf ( Result: %d\n, result) ; return 0: } cilk int fib (int n) { int x = 0; inlet void summer (int result) { x += result; return; } } if (nt2) return n; else c { summer(spawn fib (n-l)); summer(spawn fib (n-2)); sync; return (x); } 7/14
8 Cilk's compilation strategy The Cilk complier is called cilk2c Generate two clones fast little support for parallelism operates as the C elision does slow full support for parallelism, along enclosed overhead 8/14
9 Cilk's scheduling algorithm worker (processor) Maintains a ready deque A double ended queue of ready procedures Has a head and a tail Operates on its local tail end of the queue If the queue is empty (idle) it change to a thief thief Steal procedures from other workers Take form the head end of the other workers queue victim Worker losing procedures 9/14
10 Cilk's scheduling algorithm When a procedure is spawned start the as a fast clone if the procedures is getting stolen converted to a slow clone The stealing from the head, and convert to slow clone invariant that fast has not been stolen no descendants of a fast clone has been stolen 10/14
11 Stealing procedures Thief can get a procedure and change head to 3 Victim can change tail to 5 and get a procedure Victim will change tail to 4, if no thieves interfere, victim gets the procedure, if there are then If thief finds H>T, it stops trying If victim finds H>T, it restart and try again If thief try to steal, it fails if the victim try, it fails and control return to the runtime system 11/14
12 Conclusion Cilk-5 has better use of the work-first principle You can remove the Cilk keyword in C program and run it like a normal C program It uses compiled clones and queues to enhance the parallel performance 12/14
13 My comments More difficult than first expected Give a good overview over how some of the features in Cilk-5 is implemented The Cilk language seem so difficult to program i, if the user do understand C 13/14
14 Your comments Thank You 14/14
Cilk. Cilk In 2008, ACM SIGPLAN awarded Best influential paper of Decade. Cilk : Biggest principle
CS528 Slides are adopted from http://supertech.csail.mit.edu/cilk/ Charles E. Leiserson A Sahu Dept of CSE, IIT Guwahati HPC Flow Plan: Before MID Processor + Super scalar+ Vector Unit Serial C/C++ Coding
More informationBrushing the Locks out of the Fur: A Lock-Free Work Stealing Library Based on Wool
Brushing the Locks out of the Fur: A Lock-Free Work Stealing Library Based on Wool Håkan Sundell School of Business and Informatics University of Borås, 50 90 Borås E-mail: Hakan.Sundell@hb.se Philippas
More informationMultithreaded Programming in Cilk. Matteo Frigo
Multithreaded Programming in Cilk Matteo Frigo Multicore challanges Development time: Will you get your product out in time? Where will you find enough parallel-programming talent? Will you be forced to
More informationCILK/CILK++ AND REDUCERS YUNMING ZHANG RICE UNIVERSITY
CILK/CILK++ AND REDUCERS YUNMING ZHANG RICE UNIVERSITY 1 OUTLINE CILK and CILK++ Language Features and Usages Work stealing runtime CILK++ Reducers Conclusions 2 IDEALIZED SHARED MEMORY ARCHITECTURE Hardware
More informationMultithreaded Parallelism and Performance Measures
Multithreaded Parallelism and Performance Measures Marc Moreno Maza University of Western Ontario, London, Ontario (Canada) CS 3101 (Moreno Maza) Multithreaded Parallelism and Performance Measures CS 3101
More informationMulticore programming in CilkPlus
Multicore programming in CilkPlus Marc Moreno Maza University of Western Ontario, Canada CS3350 March 16, 2015 CilkPlus From Cilk to Cilk++ and Cilk Plus Cilk has been developed since 1994 at the MIT Laboratory
More informationPlan. 1 Parallelism Complexity Measures. 2 cilk for Loops. 3 Scheduling Theory and Implementation. 4 Measuring Parallelism in Practice
lan Multithreaded arallelism and erformance Measures Marc Moreno Maza University of Western Ontario, London, Ontario (Canada) CS 3101 1 2 cilk for Loops 3 4 Measuring arallelism in ractice 5 Announcements
More informationMultithreaded Programming in. Cilk LECTURE 1. Charles E. Leiserson
Multithreaded Programming in Cilk LECTURE 1 Charles E. Leiserson Supercomputing Technologies Research Group Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology
More informationHåkan Sundell University College of Borås Parallel Scalable Solutions AB
Brushing the Locks out of the Fur: A Lock-Free Work Stealing Library Based on Wool Håkan Sundell University College of Borås Parallel Scalable Solutions AB Philippas Tsigas Chalmers University of Technology
More informationCSE 260 Lecture 19. Parallel Programming Languages
CSE 260 Lecture 19 Parallel Programming Languages Announcements Thursday s office hours are cancelled Office hours on Weds 2p to 4pm Jing will hold OH, too, see Moodle Scott B. Baden /CSE 260/ Winter 2014
More informationA Quick Introduction To The Intel Cilk Plus Runtime
A Quick Introduction To The Intel Cilk Plus Runtime 6.S898: Advanced Performance Engineering for Multicore Applications March 8, 2017 Adapted from slides by Charles E. Leiserson, Saman P. Amarasinghe,
More informationPlan. Introduction to Multicore Programming. Plan. University of Western Ontario, London, Ontario (Canada) Multi-core processor CPU Coherence
Plan Introduction to Multicore Programming Marc Moreno Maza University of Western Ontario, London, Ontario (Canada) CS 3101 1 Multi-core Architecture 2 Race Conditions and Cilkscreen (Moreno Maza) Introduction
More informationCilk Plus: Multicore extensions for C and C++
Cilk Plus: Multicore extensions for C and C++ Matteo Frigo 1 June 6, 2011 1 Some slides courtesy of Prof. Charles E. Leiserson of MIT. Intel R Cilk TM Plus What is it? C/C++ language extensions supporting
More informationTable of Contents. Cilk
Table of Contents 212 Introduction to Parallelism Introduction to Programming Models Shared Memory Programming Message Passing Programming Shared Memory Models Cilk TBB HPF Chapel Fortress Stapl PGAS Languages
More informationReducers and other Cilk++ hyperobjects
Reducers and other Cilk++ hyperobjects Matteo Frigo (Intel) ablo Halpern (Intel) Charles E. Leiserson (MIT) Stephen Lewin-Berlin (Intel) August 11, 2009 Collision detection Assembly: Represented as a tree
More informationPlan. Introduction to Multicore Programming. Plan. University of Western Ontario, London, Ontario (Canada) Marc Moreno Maza CS CS 9624
Plan Introduction to Multicore Programming Marc Moreno Maza University of Western Ontario, London, Ontario (Canada) CS 4435 - CS 9624 1 Multi-core Architecture Multi-core processor CPU Cache CPU Coherence
More informationPlan. 1 Parallelism Complexity Measures. 2 cilk for Loops. 3 Scheduling Theory and Implementation. 4 Measuring Parallelism in Practice
lan Multithreaded arallelism and erformance Measures Marc Moreno Maza University of Western Ontario, London, Ontario (Canada) CS 4435 - CS 9624 1 2 cilk for Loops 3 4 Measuring arallelism in ractice 5
More informationAtomic Transactions in Cilk Project Presentation 12/1/03
Atomic Transactions in Cilk 6.895 Project Presentation 12/1/03 Data Races and Nondeterminism int x = 0; 1: read x 1: write x time cilk void increment() { x = x + 1; cilk int main() { spawn increment();
More informationEfficient Work Stealing for Fine-Grained Parallelism
Efficient Work Stealing for Fine-Grained Parallelism Karl-Filip Faxén Swedish Institute of Computer Science November 26, 2009 Task parallel fib in Wool TASK 1( int, fib, int, n ) { if( n
More informationPlan. 1 Parallelism Complexity Measures. 2 cilk for Loops. 3 Scheduling Theory and Implementation. 4 Measuring Parallelism in Practice
lan Multithreaded arallelism and erformance Measures Marc Moreno Maza University of Western Ontario, London, Ontario (Canada) CS 02 - CS 9535 arallelism Complexity Measures 2 cilk for Loops 3 Measuring
More informationThe Implementation of the Cilk-5 Multithreaded Language
The Implementation of the Cilk-5 Multithreaded Language Matte0 Frigo Charles E. Leiserson Keith H. Randall MIT Laboratory for Computer Science 545 Technology Square Cambridge, Massachusetts 02139 {athena,cel,randall}@lcs.mit.edu
More informationCS 240A: Shared Memory & Multicore Programming with Cilk++
CS 240A: Shared Memory & Multicore rogramming with Cilk++ Multicore and NUMA architectures Multithreaded rogramming Cilk++ as a concurrency platform Work and Span Thanks to Charles E. Leiserson for some
More informationCost Model: Work, Span and Parallelism
CSE 539 01/15/2015 Cost Model: Work, Span and Parallelism Lecture 2 Scribe: Angelina Lee Outline of this lecture: 1. Overview of Cilk 2. The dag computation model 3. Performance measures 4. A simple greedy
More informationShared-memory Parallel Programming with Cilk Plus
Shared-memory Parallel Programming with Cilk Plus John Mellor-Crummey Department of Computer Science Rice University johnmc@rice.edu COMP 422/534 Lecture 4 30 August 2018 Outline for Today Threaded programming
More informationConcepts in. Programming. The Multicore- Software Challenge. MIT Professional Education 6.02s Lecture 1 June 8 9, 2009
Concepts in Multicore Programming The Multicore- Software Challenge MIT Professional Education 6.02s Lecture 1 June 8 9, 2009 2009 Charles E. Leiserson 1 Cilk, Cilk++, and Cilkscreen, are trademarks of
More informationAn Overview of Parallel Computing
An Overview of Parallel Computing Marc Moreno Maza University of Western Ontario, London, Ontario (Canada) CS2101 Plan 1 Hardware 2 Types of Parallelism 3 Concurrency Platforms: Three Examples Cilk CUDA
More informationThe Cilk part is a small set of linguistic extensions to C/C++ to support fork-join parallelism. (The Plus part supports vector parallelism.
Cilk Plus The Cilk part is a small set of linguistic extensions to C/C++ to support fork-join parallelism. (The Plus part supports vector parallelism.) Developed originally by Cilk Arts, an MIT spinoff,
More informationMoore s Law. Multicore Programming. Vendor Solution. Power Density. Parallelism and Performance MIT Lecture 11 1.
Moore s Law 1000000 Intel CPU Introductions 6.172 Performance Engineering of Software Systems Lecture 11 Multicore Programming Charles E. Leiserson 100000 10000 1000 100 10 Clock Speed (MHz) Transistors
More informationParallelism and Performance
6.172 erformance Engineering of Software Systems LECTURE 13 arallelism and erformance Charles E. Leiserson October 26, 2010 2010 Charles E. Leiserson 1 Amdahl s Law If 50% of your application is parallel
More informationA Minicourse on Dynamic Multithreaded Algorithms
Introduction to Algorithms December 5, 005 Massachusetts Institute of Technology 6.046J/18.410J Professors Erik D. Demaine and Charles E. Leiserson Handout 9 A Minicourse on Dynamic Multithreaded Algorithms
More informationLace: non-blocking split deque for work-stealing
Lace: non-blocking split deque for work-stealing Tom van Dijk and Jaco van de Pol Formal Methods and Tools, Dept. of EEMCS, University of Twente P.O.-box 217, 7500 AE Enschede, The Netherlands {t.vandijk,vdpol}@cs.utwente.nl
More informationShared-memory Parallel Programming with Cilk Plus
Shared-memory Parallel Programming with Cilk Plus John Mellor-Crummey Department of Computer Science Rice University johnmc@rice.edu COMP 422/534 Lecture 4 19 January 2017 Outline for Today Threaded programming
More informationWork-Stealing by Stealing States from Live Stack Frames of a Running Application
Work-Stealing by Stealing States from Live Stack Frames of a Running Application Vivek Kumar Daniel Frampton David Grove Olivier Tardieu Stephen M. Blackburn Australian National University IBM T.J. Watson
More informationCilk: Ecient Multithreaded Computing by Keith H. Randall Submitted to the Department of Electrical Engineering and Computer Science on May 21, 1998, i
Cilk: Ecient Multithreaded Computing by Keith H. Randall Submitted to the Department of Electrical Engineering and Computer Science in partial fulllment of the requirements for the degree of Doctor of
More informationProvably Efficient Non-Preemptive Task Scheduling with Cilk
Provably Efficient Non-Preemptive Task Scheduling with Cilk V. -Y. Vee and W.-J. Hsu School of Applied Science, Nanyang Technological University Nanyang Avenue, Singapore 639798. Abstract We consider the
More informationEfficient Detection of Determinacy Race in Transactional Cilk Programs
Efficient Detection of Determinacy Race in Transactional Cilk Programs Xie Yong Singapore-MIT Alliance Outline Definition determinacy race in transactional Cilk Algorithm T. E. R. D. Implementation Cilk
More informationAn Implementation of Exception Handling with Collateral Task Abortion
[DOI: 10.2197/ipsjjip.24.439] Regular Paper An Implementation of Exception Handling with Collateral Task Abortion Tasuku Hiraishi 1,a) Shingo Okuno 2 Masahiro Yasugi 3 Received: July 3, 2015, Accepted:
More informationOverview Parallel Algorithms. New parallel supports Interactive parallel computation? Any application is parallel :
Overview Parallel Algorithms 2! Machine model and work-stealing!!work and depth! Design and Implementation! Fundamental theorem!! Parallel divide & conquer!! Examples!!Accumulate!!Monte Carlo simulations!!prefix/partial
More informationCS CS9535: An Overview of Parallel Computing
CS4403 - CS9535: An Overview of Parallel Computing Marc Moreno Maza University of Western Ontario, London, Ontario (Canada) January 10, 2017 Plan 1 Hardware 2 Types of Parallelism 3 Concurrency Platforms:
More informationIDENTIFYING PERFORMANCE BOTTLENECKS IN WORK-STEALING COMPUTATIONS
C OV ER F E AT U RE IDENTIFYING PERFORMANCE BOTTLENECKS IN WORK-STEALING COMPUTATIONS Nathan R. Tallent and John M. Mellor-Crummey, Rice University Work stealing is an effective load-balancing strategy
More informationParallel Algorithms. Design and Implementation. Jean-Louis.Roch at imag.fr. MOAIS / Lab. Informatique Grenoble, INRIA, France
Parallel Algorithms Design and Implementation Jean-Louis.Roch at imag.fr MOAIS / Lab. Informatique Grenoble, INRIA, France 1 Overview 2 Machine model and work-stealing! Work and depth! Fundamental theorem
More informationMultithreaded Parallelism on Multicore Architectures
Multithreaded Parallelism on Multicore Architectures Marc Moreno Maza University of Western Ontario, Canada CS2101 March 2012 Plan 1 Multicore programming Multicore architectures 2 Cilk / Cilk++ / Cilk
More informationCellCilk: Extending Cilk for heterogeneous multicore platforms
CellCilk: Extending Cilk for heterogeneous multicore platforms Tobias Werth 1, Silvia Schreier 2, and Michael Philippsen 1 1 University of Erlangen-Nuremberg, Germany, Computer Science Department, Programming
More informationOn the cost of managing data flow dependencies
On the cost of managing data flow dependencies - program scheduled by work stealing - Thierry Gautier, INRIA, EPI MOAIS, Grenoble France Workshop INRIA/UIUC/NCSA Outline Context - introduction of work
More informationAn Architectural Framework for Accelerating Dynamic Parallel Algorithms on Reconfigurable Hardware
An Architectural Framework for Accelerating Dynamic Parallel Algorithms on Reconfigurable Hardware Tao Chen, Shreesha Srinath Christopher Batten, G. Edward Suh Computer Systems Laboratory School of Electrical
More informationProject 3. Building a parallelism profiler for Cilk computations CSE 539. Assigned: 03/17/2015 Due Date: 03/27/2015
CSE 539 Project 3 Assigned: 03/17/2015 Due Date: 03/27/2015 Building a parallelism profiler for Cilk computations In this project, you will implement a simple serial tool for Cilk programs a parallelism
More informationMetaFork: A Compilation Framework for Concurrency Platforms Targeting Multicores
MetaFork: A Compilation Framework for Concurrency Platforms Targeting Multicores Presented by Xiaohui Chen Joint work with Marc Moreno Maza, Sushek Shekar & Priya Unnikrishnan University of Western Ontario,
More informationUnderstanding Task Scheduling Algorithms. Kenjiro Taura
Understanding Task Scheduling Algorithms Kenjiro Taura 1 / 48 Contents 1 Introduction 2 Work stealing scheduler 3 Analyzing execution time of work stealing 4 Analyzing cache misses of work stealing 5 Summary
More informationTaskMan: Simple Task-Parallel Programming
TaskMan: Simple Task-Parallel Programming Derek Hower University of Wisconsin-Madison Computer Sciences Dept. 1210 W. Dayton St. Madison, WI drh5@cs.wisc.edu Steve Jackson University of Wisconsin-Madison
More informationMultithreaded Algorithms Part 1. Dept. of Computer Science & Eng University of Moratuwa
CS4460 Advanced d Algorithms Batch 08, L4S2 Lecture 11 Multithreaded Algorithms Part 1 N. H. N. D. de Silva Dept. of Computer Science & Eng University of Moratuwa Announcements Last topic discussed is
More informationEffective Performance Measurement and Analysis of Multithreaded Applications
Effective Performance Measurement and Analysis of Multithreaded Applications Nathan Tallent John Mellor-Crummey Rice University CSCaDS hpctoolkit.org Wanted: Multicore Programming Models Simple well-defined
More informationCilk, Matrix Multiplication, and Sorting
6.895 Theory of Parallel Systems Lecture 2 Lecturer: Charles Leiserson Cilk, Matrix Multiplication, and Sorting Lecture Summary 1. Parallel Processing With Cilk This section provides a brief introduction
More informationOwnership of a queue for practical lock-free scheduling
Ownership of a queue for practical lock-free scheduling Lincoln Quirk May 4, 2008 Abstract We consider the problem of scheduling tasks in a multiprocessor. Tasks cannot always be scheduled independently
More informationCOMP Parallel Computing. SMM (4) Nested Parallelism
COMP 633 - Parallel Computing Lecture 9 September 19, 2017 Nested Parallelism Reading: The Implementation of the Cilk-5 Multithreaded Language sections 1 3 1 Topics Nested parallelism in OpenMP and other
More informationA Primer on Scheduling Fork-Join Parallelism with Work Stealing
Doc. No.: N3872 Date: 2014-01-15 Reply to: Arch Robison A Primer on Scheduling Fork-Join Parallelism with Work Stealing This paper is a primer, not a proposal, on some issues related to implementing fork-join
More informationAtomic Transactions in Cilk
Atomic Transactions in Jim Sukha 12-13-03 Contents 1 Introduction 2 1.1 Determinacy Races in Multi-Threaded Programs......................... 2 1.2 Atomicity through Transactions...................................
More informationMulticore Programming Handout 1: Installing GCC Cilk Plus
Multicore Programming Handout 1: Installing GCC Cilk Plus Leo Ferres Department of Computer Science Universidad de Concepción Email: lferres@inf.udec.cl February 19, 2013 1 Introduction For our lab work,
More information1 Optimizing parallel iterative graph computation
May 15, 2012 1 Optimizing parallel iterative graph computation I propose to develop a deterministic parallel framework for performing iterative computation on a graph which schedules work on vertices based
More informationCilk Reference Manual
Cilk 5.4.6 Reference Manual Supercomputing Technologies Group MIT Laboratory for Computer Science http://supertech.lcs.mit.edu/cilk Cilk is a trademark of the Massachusetts Institute of Technology. The
More informationCilk: An Efficient Multithreaded Runtime System
Cilk: An Efficient Multithreaded Runtime System ROBERT D. BLUMOFE, CHRISTOPHER F. JOERG, BRADLEY C. KUSZMAUL, CHARLES E. LEISERSON, KEITH H. RANDALL, AND YULI ZHOU MIT Laboratory for Computer Science,
More informationCilk: An Efficient Multithreaded Runtime System
Cilk: An Efficient Multithreaded Runtime System ROBERT D. BLUMOFE, CHRISTOPHER F. JOERG, BRADLEY C. KUSZMAUL, CHARLES E. LEISERSON, KEITH H. RANDALL, AND YULI ZHOU MIT Laboratory for Computer Science,
More informationCompsci 590.3: Introduction to Parallel Computing
Compsci 590.3: Introduction to Parallel Computing Alvin R. Lebeck Slides based on this from the University of Oregon Admin Logistics Homework #3 Use script Project Proposals Document: see web site» Due
More informationDynamic Processor Allocation for Adaptively Parallel Work-Stealing Jobs. Siddhartha Sen
Dynamic Processor Allocation for Adaptively Parallel Work-Stealing Jobs by Siddhartha Sen Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements
More informationThe JCilk Multithreaded Language. I-Ting Angelina Lee
The JCilk Multithreaded Language by I-Ting Angelina Lee Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Master of
More informationCSCE 313 Introduction to Computer Systems. Instructor: Dezhen Song
CSCE 313 Introduction to Computer Systems Instructor: Dezhen Song Programs, Processes, and Threads Programs and Processes Threads Programs, Processes, and Threads Programs and Processes Threads Processes
More informationCSE 613: Parallel Programming
CSE 613: Parallel Programming Lecture 3 ( The Cilk++ Concurrency Platform ) ( inspiration for many slides comes from talks given by Charles Leiserson and Matteo Frigo ) Rezaul A. Chowdhury Department of
More informationCSCE 313: Intro to Computer Systems
CSCE 313 Introduction to Computer Systems Instructor: Dr. Guofei Gu http://courses.cse.tamu.edu/guofei/csce313/ Programs, Processes, and Threads Programs and Processes Threads 1 Programs, Processes, and
More informationReading Assignment 4. n Chapter 4 Threads, due 2/7. 1/31/13 CSE325 - Processes 1
Reading Assignment 4 Chapter 4 Threads, due 2/7 1/31/13 CSE325 - Processes 1 What s Next? 1. Process Concept 2. Process Manager Responsibilities 3. Operations on Processes 4. Process Scheduling 5. Cooperating
More informationAdaptively Parallel Processor Allocation for Cilk Jobs
6.895 Theory of Parallel Systems Kunal Agrawal, Siddhartha Sen Final Report Adaptively Parallel Processor Allocation for Cilk Jobs Abstract An adaptively parallel job is one in which the number of processors
More informationSpeculative Parallelism in Cilk++
Speculative Parallelism in Cilk++ Ruben Perez MIT rmperez@mit.edu Gregory Malecha Harvard University SEAS gmalecha@cs.harvard.edu ABSTRACT Backtracking search algorithms are useful in many domains, from
More informationComputer Systems Assignment 2: Fork and Threads Package
Autumn Term 2018 Distributed Computing Computer Systems Assignment 2: Fork and Threads Package Assigned on: October 5, 2018 Due by: October 12, 2018 1 Understanding fork() and exec() Creating new processes
More informationScheduling Parallel Programs by Work Stealing with Private Deques
Scheduling Parallel Programs by Work Stealing with Private Deques Umut Acar Carnegie Mellon University Arthur Charguéraud INRIA Mike Rainey Max Planck Institute for Software Systems PPoPP 25.2.2013 1 Scheduling
More informationShared-memory Parallel Programming with Cilk Plus (Parts 2-3)
Shared-memory Parallel Programming with Cilk Plus (Parts 2-3) John Mellor-Crummey Department of Computer Science Rice University johnmc@rice.edu COMP 422/534 Lecture 5-6 24,26 January 2017 Last Thursday
More informationIntroduction to Multithreaded Algorithms
Introduction to Multithreaded Algorithms CCOM5050: Design and Analysis of Algorithms Chapter VII Selected Topics T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein. Introduction to algorithms, 3 rd
More informationAMCAT Automata Coding Sample Questions And Answers
1) Find the syntax error in the below code without modifying the logic. #include int main() float x = 1.1; switch (x) case 1: printf( Choice is 1 ); default: printf( Invalid choice ); return
More informationBeyond Nested Parallelism: Tight Bounds on Work-Stealing Overheads for Parallel Futures
Beyond Nested Parallelism: Tight Bounds on Work-Stealing Overheads for Parallel Futures Daniel Spoonhower Guy E. Blelloch Phillip B. Gibbons Robert Harper Carnegie Mellon University {spoons,blelloch,rwh}@cs.cmu.edu
More informationLoad Balancing. Minsoo Ryu. Department of Computer Science and Engineering. Hanyang University. Real-Time Computing and Communications Lab.
Load Balancing Minsoo Ryu Department of Computer Science and Engineering 2 1 Concepts of Load Balancing Page X 2 Load Balancing Algorithms Page X 3 Overhead of Load Balancing Page X 4 Load Balancing in
More informationThreads. What is a thread? Motivation. Single and Multithreaded Processes. Benefits
CS307 What is a thread? Threads A thread is a basic unit of CPU utilization contains a thread ID, a program counter, a register set, and a stack shares with other threads belonging to the same process
More informationA Fast Fourier Transform Compiler
RETROSPECTIVE: A Fast Fourier Transform Compiler Matteo Frigo Vanu Inc., One Porter Sq., suite 18 Cambridge, MA, 02140, USA athena@fftw.org 1. HOW FFTW WAS BORN FFTW (the fastest Fourier transform in the
More informationUni-Address Threads: Scalable Thread Management for RDMA-based Work Stealing
Uni-Address Threads: Scalable Thread Management for RDMA-based Work Stealing Shigeki Akiyama, Kenjiro Taura The University of Tokyo June 17, 2015 HPDC 15 Lightweight Threads Lightweight threads enable
More informationThread Scheduling for Multiprogrammed Multiprocessors
Thread Scheduling for Multiprogrammed Multiprocessors (Authors: N. Arora, R. Blumofe, C. G. Plaxton) Geoff Gerfin Dept of Computer & Information Sciences University of Delaware Outline Programming Environment
More informationEfficiently Detecting Races in Cilk Programs That Use Reducer Hyperobjects
Efficiently Detecting Races in Cilk Programs That Use Reducer Hyperobjects ABSTRACT I-Ting Angelina Lee Washington University in St. Louis One Brookings Drive St. Louis, MO 63130 A multithreaded Cilk program
More informationThe DAG Model; Analysis of For-Loops; Reduction
CSE341T 09/06/2017 Lecture 3 The DAG Model; Analysis of For-Loops; Reduction We will now formalize the DAG model. We will also see how parallel for loops are implemented and what are reductions. 1 The
More informationDue: What to submit: Background
Due: See website for due date. (Late days may be used.) What to submit: Upload a tar ball using the p2 identifier that includes the following files: - id.txt with the SLO IDs in the format described for
More informationCSE 539S, Spring 2015 Concepts in Mul9core Compu9ng Lecture 1: Introduc9on
CSE 539S, Spring 2015 Concepts in Mul9core Compu9ng Lecture 1: Introduc9on I- Ting Angelina Lee Jan 13, 2015 Technology Scaling 10,000,000 1,000,000 100,000 10,000 1,000 100 10 1 u Transistors x 1000 Clock
More informationOpenMP Overview. in 30 Minutes. Christian Terboven / Aachen, Germany Stand: Version 2.
OpenMP Overview in 30 Minutes Christian Terboven 06.12.2010 / Aachen, Germany Stand: 03.12.2010 Version 2.3 Rechen- und Kommunikationszentrum (RZ) Agenda OpenMP: Parallel Regions,
More informationBIL 104E Introduction to Scientific and Engineering Computing. Lecture 14
BIL 104E Introduction to Scientific and Engineering Computing Lecture 14 Because each C program starts at its main() function, information is usually passed to the main() function via command-line arguments.
More informationDefinition Multithreading Models Threading Issues Pthreads (Unix)
Chapter 4: Threads Definition Multithreading Models Threading Issues Pthreads (Unix) Solaris 2 Threads Windows 2000 Threads Linux Threads Java Threads 1 Thread A Unix process (heavy-weight process HWP)
More informationEfficiently Scheduling Task Dataflow Parallelism: A Comparison Between Swan and QUARK
Comparison Between and, H. (215). Efficiently Scheduling Task Dataflow Parallelism: A Comparison Between and. In Proceedings of the Exascale Applications and Software Conference 215 (pp. 36-41). Edinburgh:
More informationIntroduction to Computing Systems Fall Lab # 3
EE 1301 UMN Introduction to Computing Systems Fall 2013 Lab # 3 Collaboration is encouraged. You may discuss the problems with other students, but you must write up your own solutions, including all your
More informationCISC2200 Threads Spring 2015
CISC2200 Threads Spring 2015 Process We learn the concept of process A program in execution A process owns some resources A process executes a program => execution state, PC, We learn that bash creates
More informationCS140 Final Project. Nathan Crandall, Dane Pitkin, Introduction:
Nathan Crandall, 3970001 Dane Pitkin, 4085726 CS140 Final Project Introduction: Our goal was to parallelize the Breadth-first search algorithm using Cilk++. This algorithm works by starting at an initial
More informationWhat Is A Process? Process States. Process Concept. Process Control Block (PCB) Process State Transition Diagram 9/6/2013. Process Fundamentals
What Is A Process? A process is a program in execution. Process Fundamentals #include int main(int argc, char*argv[]) { int v; printf( hello world\n ); scanf( %d, &v); return 0; Program test
More informationIntroduction to OpenMP
Christian Terboven, Dirk Schmidl IT Center, RWTH Aachen University Member of the HPC Group terboven,schmidl@itc.rwth-aachen.de IT Center der RWTH Aachen University History De-facto standard for Shared-Memory
More informationUnder the Hood, Part 1: Implementing Message Passing
Lecture 27: Under the Hood, Part 1: Implementing Message Passing Parallel Computer Architecture and Programming CMU 15-418/15-618, Today s Theme Message passing model (abstraction) Threads operate within
More informationFunctions. CS10001: Programming & Data Structures. Sudeshna Sarkar Professor, Dept. of Computer Sc. & Engg., Indian Institute of Technology Kharagpur
Functions CS10001: Programming & Data Structures Sudeshna Sarkar Professor, Dept. of Computer Sc. & Engg., Indian Institute of Technology Kharagpur 1 Recursion A process by which a function calls itself
More informationData-Race Detection in Transactions- Everywhere Parallel Programming
Data-Race Detection in Transactions- Everywhere Parallel Programming by Kai Huang B.S. Computer Science and Engineering, B.S. Mathematics Massachusetts Institute of Technology, June 2002 Submitted to the
More informationRuntime Support for Scalable Task-parallel Programs
Runtime Support for Scalable Task-parallel Programs Pacific Northwest National Lab xsig workshop May 2018 http://hpc.pnl.gov/people/sriram/ Single Program Multiple Data int main () {... } 2 Task Parallelism
More informationA Cilk Implementation of LTE Base-station uplink on the TILEPro64 Processor
A Cilk Implementation of LTE Base-station uplink on the TILEPro64 Processor Master of Science Thesis in the Programme of Integrated Electronic System Design HAO LI Chalmers University of Technology Department
More informationSample Problems for Quiz # 2
EE 1301 UMN Introduction to Computing Systems Fall 2013 Sample Problems for Quiz # 2 (with solutions) Here are sample problems to help you prepare for Quiz 2 on Oct. 31. 1. Bit-Level Arithmetic (a) Consider
More information