Provided by: University of Wisconsin-La Crosse
Date Added: Dec 2013
General Purpose computing on GPUs (GPGPU) has experienced rapid growth over the last several years as new application realms are explored and traditional highly parallel algorithms are adapted to this computational substrate. However, a large portion of the parallel workload space, both in emerging and traditional domains, remains ill-suited for GPGPU deployment due to high reliance on atomic operations, particularly as global synchronization mechanisms. Unlike the sophisticated synchronization primitives available on supercomputers, GPGPU applications must rely on slow atomic operations on shared data.