A Formal Instruction-level GPU Model for Scalable Verification

GPUs have been widely used to accelerate big-data inference applications and scientific computing through their parallelized hardware resources and programming model. Their extreme parallelism increases the possibility of bugs such as data races and un-coalesced memory accesses, and thus verifying p...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) s. 1 - 8
Hlavní autori: Xing, Yue, Huang, Bo-Yuan, Gupta, Aarti, Malik, Sharad
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: ACM 01.11.2018
Predmet:
Computational modeling
Graphics processing units
Hardware
Instruction sets
Programming
Registers
Synchronization
ISSN:1558-2434
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:GPUs have been widely used to accelerate big-data inference applications and scientific computing through their parallelized hardware resources and programming model. Their extreme parallelism increases the possibility of bugs such as data races and un-coalesced memory accesses, and thus verifying program correctness is critical. State-of-the-art GPU program verification efforts mainly focus on analyzing application-level programs, e.g., in C, and suffer from the following limitations: (1) high false-positive rate due to coarse-grained abstraction of synchronization primitives, (2) high complexity of reasoning about pointer arithmetic, and (3) keeping up with an evolving API for developing application-level programs. In this paper, we address these limitations by modeling GPUs and reasoning about programs at the instruction level. We formally model the Nvidia GPU at the parallel execution thread (PTX) level using the recently proposed Instruction-Level Abstraction (ILA) model for accelerators. PTX is analogous to the Instruction-Set Architecture (ISA) of a general-purpose processor. Our formal ILA model of the GPU includes non-synchronization instructions as well as all synchronization primitives, enabling us to verify multithreaded programs. We demonstrate the applicability of our ILA model in scalable GPU program verification of data-race checking. The evaluation shows that our checker outperforms state-of-the-art GPU data race checkers with fewer false-positives and improved scalability.
ISSN:1558-2434
DOI:10.1145/3240765.3240771