Transactional Memory and Language Support for General Purpose Graphics Processors

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Recent shifts in technologyare creating a looming challenge for Canadian software developers. Increasing power dissipation is preventing substantial increases in clock frequencyfor the CPU at the heart of computers. While previouslymeeting often unstated software performance objectives was simplya matter of waiting for the next CPU generation to reach the market, software developers are now increasinglybeing forced to write software for the class of more power-efficient computing devices which process tasks explicitlyin parallel. These devices are exemplified bythe "manycore" Graphics Processing Units (GPUs) found in most personal computers sold today. Fundamentally, GPUs can offer an order of magnitude more efficient computation than CPUs (even "multicore" CPUs) because theydevote more silicon resources to the actual job of computation rather than to discovering what tasks might be parallel. Consequently, a rapidlygrowing number of "GPU compute" clusters are now being installed in large companies around the world, and GPU manufacturers (such as ATI/AMD in Canada) have begun providing programming interfaces to make it easier to write software to use GPUs for non-graphics computations. However, writing software for GPUs remains challenging due to the need for software developers to express parallelism and tune their code for the complex behavior of the hardware. Our research addresses these problems in three ways: bydeveloping hardware and software for delivering more predictable performance to software developers; bysupporting a programming model called transactional memorydirectlyin GPU hardware; and bydeveloping programming language support to ease the process of restructuring code, or automating the restructuring altogether. Together these changes will make GPUs easier to use in existing and future application domains. Our research will directlybenefit Canadian GPU manufacturers and software companies faced with meeting the challenges resulting from the shift towardsparallel computing systems.