EDBT 2026 Demo / reviewers in the wild / expert
Harmit Raval
dblp:301/9463
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
1 paper |
Concurrent programming · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
GPUs and heterogeneous computing · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Concurrent programming › concurrency correctness
progress guarantees |
0.5 | 1 | 2021 | Specifying and testing GPU workgroup progress models · Proc. ACM Program. Lang. 2021 |
GPUs and heterogeneous computing › GPU programming
GPU programming models |
0.5 | 1 | 2021 | Specifying and testing GPU workgroup progress models · Proc. ACM Program. Lang. 2021 |
Methods — techniques the papers use, named apart from their topics
termination oracle · 1.0litmus testing · 1.0formal semantics · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Specifying and testing GPU workgroup progress modelsabstractAs 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. Tyler Sorensen 0001, Lucas F. Salvador, Harmit Raval, Hugues Evrard, John Wickerson, Margaret Martonosi, Alastair F. Donaldson |
Proc. ACM Program. Lang. | 3 |