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Title: Compiler-Assisted Test Acceleration on GPUs (for Embedded Software)

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Speaker: Vanya Yaneva

  • Lab Lunch
When Jan 16, 2018
from 01:00 PM to 02:00 PM
Where Mini Forum 2 (MF2) Level 4
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Embedded software is found everywhere from our highly
visible mobile devices to the confines of our car in the form of smart
sensors.  Embedded software companies are under huge pressure to
produce safe applications that limit risks, and testing is absolutely
critical to alleviate concerns regarding safety and user privacy.
This requires using large test suites throughout the development
process, increasing time-to-market and ultimately hindering

Speeding up test execution is, therefore, of paramount importance for
embedded software developers.  This is traditionally achieved by
running, in parallel, multiple tests on large-scale clusters of
computers.  However, this approach is costly in terms of
infrastructure maintenance and energy consumed, and is at times
inconvenient as developers have to wait for their tests to be
scheduled on a shared resource.

I look at exploiting GPUs (Graphics Processing Units) for running
embedded software testing.  GPUs are readily available in most
computers and offer tremendous amounts of parallelism, making them an
ideal target for embedded software testing.  However, they use
specialist programming models, which limits their scope and makes them
notoriously difficult to program.  To mitigate these issues, I propose
a compiler-assisted approach which automatically compiles the C
program into GPU kernels and executes their tests in parallel on the
GPU threads.  Current evaluation across nine programs from an industry
standard embedded benchmark suite achieves an average speedup of 16x
when compared to CPU execution.

In this talk, I will present this approach, together with current
evaluation results and some ideas for future work. Papers (both with coauthors Ajitha Rajan and Christophe Dubach):
Compiler-Assisted Test Acceleration on GPUs for Embedded Software (ISSTA'17)
ParTeCL: Parallel Testing Using OpenCL (ISSTA'17 tools demo)

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