'Data races' presentation slideshows

Valor: Efficient, Software-Only Region Conflict Exceptions

Valor: Efficient, Software-Only Region Conflict Exceptions. Swarnendu Biswas , Minjia Zhang, and Michael D. Bond Ohio State University Brandon Lucia Carnegie Mellon University OOPSLA 2015. A Simple C++ Program. X* x = NULL; bool done= false ;. Thread T1. Thread T2.

1.19k views • 90 slides

998 views • 87 slides

Programming Multi-Core Systems

892 views • 75 slides

Module B: Parallelization Techniques

Module B: Parallelization Techniques. Course TBD Lecture TBD Term TBD. Part 1: Parallel Array Operations. Finding the max/min array elements Max/min using 2 cores Alternate approach Parallelism bugs Fixing data races. Goals for this Lecture. Learn how to parallelize code

671 views • 60 slides

Ownership Types for Safe Programming: Preventing Data Races and Deadlocks

Ownership Types for Safe Programming: Preventing Data Races and Deadlocks. Laboratory for Computer Science Massachusetts Institute of Technology {chandra, rhlee, rinard}@lcs.mit.edu. Chandrasekhar Boyapati Robert Lee Martin Rinard. Data Races in Multithreaded Programs.

661 views • 54 slides

Systems and Synthetic Biology: A programming languages point of view

Systems and Synthetic Biology: A programming languages point of view. Saurabh Srivastava Assistant Research Engineer Computer Science + Bioengineering University of California, Berkeley. In perspective. One view: biological specifications. One view: syntax of components.

618 views • 53 slides

Synchronization II

Synchronization II. Hakim Weatherspoon CS 3410, Spring 2013 Computer Science Cornell University. P&H Chapter 2.11 and 5.8. Goals for Today. Synchronization Threads and processes Critical sections, race conditions, and mutexes Atomic Instructions HW support for synchronization

647 views • 52 slides

Automatic Assumption Generation for Compositional Verification

Automatic Assumption Generation for Compositional Verification. Dimitra Giannakopoulou (RIACS), Corina P?s?reanu (Kestrel) . Automated Software Engineering NASA Ames Research Center Moffett Field, CA, USA. satisfies P?. how are assumptions obtained?. Developer encodes them explicitly

544 views • 41 slides

Dan Grossman Last Updated: May 2012

A Sophomoric Introduction to Shared-Memory Parallelism and Concurrency Lecture 5 Programming with Locks and Critical Sections. Dan Grossman Last Updated: May 2012 For more information, see http://www.cs.washington.edu/homes/djg/teachingMaterials/. Outline. Done: The semantics of locks

447 views • 35 slides

Rex: Replication at the Speed of Multi-core

Rex: Replication at the Speed of Multi-core. Zhenyu Guo, Chuntao Hong , Dong Zhou*, Mao Yang, Lidong Zhou, Li Zhuang Microsoft Research CMU*. Tension between Replication and Multi-core. Most applications are multi-threaded But, to replicate, you can only use single-thread

488 views • 35 slides

SHAASTRA 2011 :: PARALLEL PROGRAMMING WORKSHOP

SHAASTRA 2011 :: PARALLEL PROGRAMMING WORKSHOP. CILK++ WITH HANDS-ON SESSION. Cilk ++ Session Outline. Introduction to cilk Basic cilk programming constructs Constructing cilk programs for common Algorithms Cilk ++ Tools. Cilk ++ Session Outline. Introduction to cilk

489 views • 34 slides

Patterns for Programming Shared Memory

Patterns for Programming Shared Memory. Keep Abstraction Level High. Temptation: ad-hoc parallelization Add tasks or parallel loops all over the code Discover data races/deadlocks, fix them one-by-one Problem: Complexity quickly adds up

509 views • 27 slides

Respec : Efficient Online Multiprocessor Replay via Speculation and External Determinism

Respec : Efficient Online Multiprocessor Replay via Speculation and External Determinism. Dongyoon Lee , Benjamin Wester , Kaushik Veeraraghavan , Satish Narayanasamy , Peter M. Chen, and Jason Flinn University of Michigan, Ann Arbor . Deterministic Replay. Deterministic Replay

386 views • 23 slides

Concurrency/synchronization using UML state models

345 views • 21 slides

Let the |> fun begin

An introduction to F# (part 2). Let the |> fun begin. Bogdan Brinzarea-Iamandi Banca Romaneasca. 25 February 2010. Agenda. Asynchronous and parallel programming. Why should these matter to me?. Why should these matter to me?. Working with threads is difficult

295 views • 21 slides

Debugging at Extreme Scale using proc ++ and TBON-FS

Debugging at Extreme Scale using proc ++ and TBON-FS. Michael Brim. What is Extreme Scale?. 100,000+ hosts 1,000,000+ processes and threads Deployed Systems K Computer: ~88,000 8-core hosts Tianhe-1A: ~7,000 12-core hosts + ~7000 accelerators Imminent Systems

324 views • 20 slides

What is the Cost of Determinism?

354 views • 20 slides

Concurrent Predicates: A Debugging Technique for Every Parallel Programmer

244 views • 16 slides

Eraser: A Dynamic Data Race Detector for Multithreaded Programs

Eraser: A Dynamic Data Race Detector for Multithreaded Programs. STEFAN SAVAGE, MICHAEL BURROWS, GREG NELSON , PATRICK SOBALVARRO and THOMAS ANDERSON. Introduction. Multithreaded programming is difficult and error prone.

457 views • 15 slides

378 views • 13 slides