GreenMM: Energy Efficient GPU Matrix Multiplication through Undervolting

The current trend of ever-increasing performance in scientific applications comes with tremendous growth in energy consumption. In this paper, we present GreenMM framework for matrix multiplication, which reduces energy consumption in GPUs through undervolting without sacrificing the performance. The idea in this paper is to undervolt the GPU beyond the minimum operating voltage (V-min) to save maximum energy while keeping the frequency constant. Since such undervolting may give rise to faults, we design an Algorithm Based Fault Tolerance (ABFT) algorithm to detect and correct those errors. We target cuBLAS Matrix Multiplication (cuBLAS-MM), as a key kernel used in many scientific applications. Empirically, we explore different errors and derive a fault model as a function of undervolting levels and matrix sizes. Then, using the model, we configure the proposed FT-cuBLAS-MM algorithm. We show that energy consumption is reduced up to 19.8%. GreenMM also improves the GFLOPS/Watt by 9% with negligible performance overhead.