Static Analysis Driven Cache Performance Testing

Banerjee, A.; Chattopadhyay, Sudipta; Roychoudhury, Abhik

doi:10.1109/rtss.2013.39

Cited by 11 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the contrary, we argue the importance of execution platforms in the context of detecting performance bugs, with a specific focus on GPUs. Previous works on performance testing (Chattopadhyay et al, 2014b;Banerjee et al, 2013) or other works on testing GPU programs (Li et al, 2012) concentrate on testinput generation and are not directly applicable for localizing the root cause of performance bugs. In this work, we aim to localize the cause of memory interferences by systematically comparing the original and the golden traces.…”

Section: Related Workmentioning

confidence: 99%

Performance and Security Analysis for GPU-Based Applications

Horga

2022

Linköping Studies in Science and Technology. Dissertations

View full text Add to dashboard Cite

Grafikprocessorer (eng. Graphics Processing Units eller GPUer) är ursprungligen hårdvaruplattformar för att accelerera återgivning av grafik på exempelvis datorskärmar. Över tid har GPUer blivit allt bättre på att processa bilddata, både i termer av hastighet och bildstorlek, samtidigt som deras strömförbrukning har minskat. Dessa egenskaper har gjort GPUer attraktiva att använda i olika domäner för att snabba upp beräkningar och databehandling även på andra typer av data än bilder. Tillämpningar där GPUer används för andra syften än att generera grafik brukar benämnas GPGPU på engelska (för general-purpose computing on graphics processing unit). GPGPU används idag inom en rad olika domäner, exempelvis i flygelektronik, i bilar, och t.o.m. inom vården. Dessa nya användningsområden medför dock nya krav på hård-och mjukvara när det gäller exempelvis prestanda och säkerhet. I den här avhandlingen föreslår vi lösningar för att hantera sådana krav. Vi presenterar olika programvaruverktyg och tekniker för att analysera mjukvara för GPGPU, och visar hur våra föreslagna lösningar kan användas för att upptäcka prestandamässiga flaskhalsar. Vi visar också hur olika egenskaper hos sådan GPGPU-mjukvara kan mätas, för att hantera olika typer av säkerhetsproblem.

show abstract

Section: Related Workmentioning

confidence: 99%

Performance and Security Analysis for GPU-Based Applications

Horga

2022

Linköping Studies in Science and Technology. Dissertations

View full text Add to dashboard Cite

show abstract

“…In this section, we illustrate effectiveness of our method on two examples: a hand-crafted program, and a benchmark for worst-case execution time analysis adapted from [4].…”

Section: Motivating Examplementioning

confidence: 99%

Abstraction-driven Concolic Testing

Daca

Gupta

Henzinger

2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Concolic testing is a promising method for generating test suites for large programs. However, it suffers from the path-explosion problem and often fails to find tests that cover difficult-to-reach parts of programs. In contrast, model checkers based on counterexample-guided abstraction refinement explore programs exhaustively, while failing to scale on large programs with precision. In this paper, we present a novel method that iteratively combines concolic testing and model checking to find a test suite for a given coverage criterion. If concolic testing fails to cover some test goals, then the model checker refines its program abstraction to prove more paths infeasible, which reduces the search space for concolic testing. We have implemented our method on top of the concolic-testing tool Crest and the model checker CpaChecker. We evaluated our tool on a collection of programs and a category of SvComp benchmarks. In our experiments, we observed an improvement in branch coverage compared to Crest from 48% to 63% in the best case, and from 66% to 71% on average.

show abstract

“…Existing works on concolic testing have mostly concentrated on sequential applications to detect functionality bugs [11,15] and more recently, to detect poor cache performance [10]. Effective testing of parallel software has also emerged to be a critical problem.…”

Section: Related Workmentioning

confidence: 99%

Automated software testing of memory performance in embedded GPUs

Chattopadhyay

Eles

Peng

2014

Proceedings of the 14th International Conference on Embedded Software

Self Cite

View full text Add to dashboard Cite

Embedded and real-time software is often constrained by several temporal requirements. Therefore, it is important to design embedded software that meets the required performance goal. The inception of embedded graphics processing units (GPUs) brings fresh hope in developing high-performance embedded software which were previously not suitable for embedded platforms. Whereas GPUs use massive parallelism to obtain high throughput, the overall performance of an application running on embedded GPUs is often limited by memory performance. Therefore, a crucial problem lies in automatically detecting the inefficiency of such software developed for embedded GPUs. In this paper, we propose GUPT, a novel test generation framework that systematically explores and detects poor memory performance of applications running on embedded GPUs. In particular, we systematically combine static analysis with dynamic test generation to expose likely execution scenarios with poor memory performance. Each test case in our generated test suite reports a potential memory-performance issue, along with the detailed information to reproduce the same. We have implemented our test generation framework using GPGPU-Sim, a cycleaccurate simulator and the LLVM compiler infrastructure. We have evaluated our framework for several open-source programs. Our experiments suggest the efficacy of our framework by exposing numerous memory-performance issues in a reasonable time. We also show the usage of our framework in improving the performance of programs for embedded GPUs.

show abstract

Static Analysis Driven Cache Performance Testing

Cited by 11 publications

References 11 publications

Performance and Security Analysis for GPU-Based Applications

Performance and Security Analysis for GPU-Based Applications

Abstraction-driven Concolic Testing

Automated software testing of memory performance in embedded GPUs

Contact Info

Product

Resources

About