Warp-Level Parallelism: Enabling Multiple Replications In Parallel on GPU

TL;DR

This paper introduces Warp-Level Parallelism (WLP), a GPU-based method that enables multiple stochastic simulation replications to run simultaneously, significantly accelerating the process compared to traditional SIMT approaches.

Contribution

The paper presents WLP, a novel GPU parallelization technique that efficiently executes multiple replications in parallel, improving speed over existing SIMT-based methods.

Findings

WLP achieves up to six times faster computation of multiple replications.

Benchmark results show significant speedup over traditional SIMT approaches.

WLP effectively leverages GPU warp-level parallelism for stochastic simulations.

Abstract

Stochastic simulations need multiple replications in order to build confidence intervals for their results. Even if we do not need a large amount of replications, it is a good practice to speed-up the whole simulation time using the Multiple Replications In Parallel (MRIP) approach. This approach usually supposes to have access to a parallel computer such as a symmetric mul-tiprocessing machine (with many cores), a computing cluster or a computing grid. In this paper, we propose Warp-Level Parallelism (WLP), a GP-GPU-enabled solution to compute MRIP on GP-GPUs (General-Purpose Graphics Processing Units). These devices display a great amount of parallel computational power at low cost, but are tuned to process efficiently the same operation on several data, through different threads. Indeed, this paradigm is called Single Instruction, Multiple Threads (SIMT). Our approach proposes to rely on small threads groups, called warps, to perform independent computations such as replications. We have benchmarked WLP with three different models: it allows MRIP to be computed up to six times faster than with the SIMT computing paradigm.