Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
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Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
Efficient Instrumentation of GPUGPU Programs using Information Flow Analysis and Symbolic Execution
Proceedings of Seventh Workshop on General-Purpose Computation on Graphics Processing Units (GPGPU-7), March 2014.
Naila Farooqui, Karsten Schwan, Sudhakar Yalamanchili
Georgia Institute of Technology
Dynamic instrumentation of GPGPU binaries makes possible real-time introspection methods for performance debugging, correctness checks, workload characterization, and runtime optimization. Such instrumentation involves inserting code at the instruction level of an application, while the application is running, thereby able to accurately profile data-dependent application behavior. Runtime overheads seen from instrumentation, however, can obviate its utility. This paper shows how a combination of information flow analysis and symbolic execution can be used to alleviate these overheads. The methods and their effectiveness are demonstrated for a variety of GPGPU codes written in OpenCL that run on AMD GPU target backends. Kernels that can be analyzed entirely via symbolic execution need not be instrumented, thus eliminating kernel runtime overheads altogether. For the remaining GPU kernels, our results show 5-38% improvements in kernel runtime overheads.
FULL PAPER: pdf