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Mon 19 Jun 2017 14:00 - 14:25 at Aula Master - Concurrency Analysis Chair(s): Jeff Huang

GPU programming models enable and encourage massively parallel programming with over a million threads, requiring extreme parallelism to achieve good performance. Massive parallelism brings significant correctness challenges by increasing the possibility for bugs as the number of thread interleavings balloons. Conventional dynamic safety analyses struggle to run at this scale.

We present Barracuda, a data race detector for GPU programs written in Nvidia’s CUDA language. Barracuda handles a wider range of parallelism constructs than previous work, including branch operations, low-level atomics and memory fences, which allows Barracuda to detect new classes of races. Barracuda operates at the binary level for increased compatibility with existing code, leveraging a new binary instrumentation framework that is extensible to other dynamic analyses. Barracuda incorporates a number of novel optimizations that are crucial for scaling data race detection to over a million threads.

Mon 19 Jun
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14:00 - 15:40: PLDI Research Papers - Concurrency Analysis at Aula Master
Chair(s): Jeff HuangTexas A&M University
pldi-2017-papers14:00 - 14:25
Ariel EizenbergUniversity of Pennsylvania, Yuanfeng PengUniversity of Pennsylvania, Toma PigliUniversity of Pennsylvania, William ManskyPrinceton University, Joseph DeviettiUniversity of Pennsylvania
pldi-2017-papers14:25 - 14:50
Dustin Rhodes, Cormac FlanaganUniversity of California, Santa Cruz, Stephen FreundWilliams College
pldi-2017-papers14:50 - 15:15
Dileep KiniUniversity of Illinois at Urbana-Champaign, Umang MathurUniversity of Illinois at Urbana-Champaign, Mahesh ViswanathanUniversity of Illinois at Urbana-Champaign
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pldi-2017-papers15:15 - 15:40
Marina BillesTU Darmstadt, Anders MøllerAarhus University, Michael PradelTU Darmstadt
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