DuctTeip: An efficient programming model for distributed task based parallel computing

TL;DR

DuctTeip introduces a hierarchical task-based programming model tailored for distributed memory systems, enhancing performance and scalability in scientific computing applications.

Contribution

The paper presents a novel hierarchical task-based programming model for distributed memory systems, extending shared memory paradigms to complex distributed architectures.

Findings

Achieves competitive performance on scientific applications

Effectively manages hierarchical data and task decomposition

Demonstrates scalability on distributed systems

Abstract

Current high-performance computer systems used for scientific computing typically combine shared memory computational nodes in a distributed memory environment. Extracting high performance from these complex systems requires tailored approaches. Task based parallel programming has been successful both in simplifying the programming and in exploiting the available hardware parallelism for shared memory systems. In this paper we focus on how to extend task parallel programming to distributed memory systems. We use a hierarchical decomposition of tasks and data in order to accommodate the different levels of hardware. We test the proposed programming model on two different applications, a Cholesky factorization, and a solver for the Shallow Water Equations. We also compare the performance of our implementation with that of other frameworks for distributed task parallel programming, and show that it is competitive.