Task Graph Transformations for Latency Tolerance

11/13/2018
by   Victor Eijkhout, et al.
0

The Integrative Model for Parallelism (IMP) derives a task graph from a higher level description of parallel algorithms. In this note we show how task graph transformations can be used to achieve latency tolerance in the program execution. We give a formal derivation of the graph transformation, and show through simulation how latency tolerant algorithms can be faster than the naive execution in a strong scaling scenario.

READ FULL TEXT
research
08/04/2022

Designing and developing tools to automatically identify parallelism

In this work we present a dynamic analysis tool for analyzing regions of...
research
07/04/2018

Cimple: Instruction and Memory Level Parallelism

Modern out-of-order processors have increased capacity to exploit instru...
research
03/27/2013

Computational Aspects of the Mobius Transform

In this paper we associate with every (directed) graph G a transformatio...
research
07/18/2020

PaSh: Light-touch Data-Parallel Shell Processing

This paper presents PaSh, a system for parallelizing POSIX shell scripts...
research
05/06/2019

Parsl: Pervasive Parallel Programming in Python

High-level programming languages such as Python are increasingly used to...
research
07/08/2019

Parallelism Theorem and Derived Rules for Parallel Coherent Transformations

An Independent Parallelism Theorem is proven in the theory of adhesive H...
research
07/09/2019

Trustworthy Graph Algorithms

The goal of the LEDA project was to build an easy-to-use and extendable ...

Please sign up or login with your details

Forgot password? Click here to reset