Exploiting Latency and Error Tolerance of GPGPU Applications for an Energy-Efficient DRAM

Abstract

Memory (DRAM) energy consumption is one of the major scalability bottlenecks for almost all computing systems, including throughput machines such as Graphics Processing Units (GPUs). A large fraction of DRAM dynamic energy is spent on fetching the data bits from a DRAM page (row) to a small-sized hardware structure called as the row buffer. The data access from this row buffer is much less expensive in terms of energy and latency. Hence, it is preferred to reuse the buffered data as much as possible before activating another row and bringing its data to these row buffers. Our thorough characterization of several GPGPU applications shows that these row buffers are poorly utilized leading to sub-optimal energy consumption. To address this, we propose a novel memory scheduling for GPUs that exploits latency and error tolerance properties of GPGPU applications to reduce row energy by 44% on average.