An Energy-Efficient GPGPU Register File Architecture Using Racetrack Memory

Extreme multi-Threading and fast thread switching in modern GPGPU require a large, power-hungry register file (RF), which quickly becomes one of major obstacles on the upscaling path of energy-efficient GPGPU computing. In this work, we propose to implement a power-efficient GPGPU RF built on the newly emerged racetrack memory. Racetrack memory has small cell area, low dynamic power, and nonvolatility. Its unique access mechanism, however, results in a long and location-dependent access latency, which offsets the energy saving benefit it introduces and probably harms the performance. In order to conquer the adverse impacts of racetrack memory based RF designs, we first propose a register mapping scheme to reduce the average access latency. Based on the register mapping, we develop a racetrack memory aware warp scheduling (RMWS) algorithm to further suppress the access latency. RMWS design includes a new write buffer structure that improves the scheduling efficiency as well as energy saving. We also investigate and optimize the design where multiple concurrent RMWS schedulers are employed. Experiment results show that our propose techniques can keep a GPGPU performance similar to the baseline with SRAM based RF while the RF energy is significantly reduced by 48.5 percent.

Duke Scholars

Author Yiran Chen Electrical and Computer Engineering

Author Hai "Helen" Li Electrical and Computer Engineering