Energy evaluation of Sparse Matrix-Vector Multiplication on GPU

Many recent studies suggest that energy efficiency should be placed as a primary design goal on par with the performance in building both the hardware and the software. As a primary step toward finding a good compromise between these two conflicting design goals, first we need to have a deep understanding about the performance and the energy of different application kernels. In this paper, we focus on evaluating the energy efficiency of the Sparse Matrix-Vector Multiplication (SpMV), a very challenging kernel given its irregular aspect both in terms of memory access and control flow. In the present work, we consider the SpMV kernel under four different sparse formats (COO, CSR, ELL, and HYB) on GPU. Our experimental results obtained by using real world sparse matrices from the University of Florida collection on an NVIDIA Maxwell GPU (GTX 980Ti) show that there is no universal best sparse format in terms of energy efficiency. Furthermore, we identified some sparsity characteristics which are related to the energy efficiency of different sparse formats.