Specifying and testing GPU workgroup progress models

Author:

Sorensen Tyler1ORCID,Salvador Lucas F.2,Raval Harmit2,Evrard Hugues3,Wickerson John4ORCID,Martonosi Margaret2,Donaldson Alastair F.4ORCID

Affiliation:

1. University of California at Santa Cruz, USA

2. Princeton University, USA

3. Google, USA

4. Imperial College London, UK

Abstract

As GPU availability has increased and programming support has matured, a wider variety of applications are being ported to these platforms. Many parallel applications contain fine-grained synchronization idioms; as such, their correct execution depends on a degree of relative forward progress between threads (or thread groups). Unfortunately, many GPU programming specifications (e.g. Vulkan and Metal) say almost nothing about relative forward progress guarantees between workgroups. Although prior work has proposed a spectrum of plausible progress models for GPUs, cross-vendor specifications have yet to commit to any model. This work is a collection of tools and experimental data to aid specification designers when considering forward progress guarantees in programming frameworks. As a foundation, we formalize a small parallel programming language that captures the essence of fine-grained synchronization. We then provide a means of formally specifying a progress model, and develop a termination oracle that decides whether a given program is guaranteed to eventually terminate with respect to a given progress model. Next, we formalize a set of constraints that describe concurrent programs that require forward progress to terminate. This allows us to synthesize a large set of 483 progress litmus tests. Combined with the termination oracle, we can determine the expected status of each litmus test -- i.e. whether it is guaranteed to eventually terminate -- under various progress models. We present a large experimental campaign running the litmus tests across 8 GPUs from 5 different vendors. Our results highlight that GPUs have significantly different termination behaviors under our test suite. Most notably, we find that Apple and ARM GPUs do not support the linear occupancy-bound model, as was hypothesized by prior work.

Publisher

Association for Computing Machinery (ACM)

Subject

Safety, Risk, Reliability and Quality,Software

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. High-Performance GPU-to-CPU Transpilation and Optimization via High-Level Parallel Constructs;Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming;2023-02-21

2. Taking Back Control in an Intermediate Representation for GPU Computing;Proceedings of the ACM on Programming Languages;2023-01-09

3. Specifying and testing GPU workgroup progress models;Proceedings of the ACM on Programming Languages;2021-10-20

4. Distributed Data Persistency;MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture;2021-10-17

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