The task of tuning parameters for optimizing performance or other metrics of interest such as energy, variability, etc. can be resource and time consuming. Presence of a large parameter space makes a comprehensive exploration infeasible. In this paper, ...

Sparse Matrix-Vector Multiplication (SpMV) is an essential computation kernel for many data-analytic workloads running in both supercomputers and data centers. The intrinsic irregularity in SpMV is challenging to achieve high performance, especially ...

With the unprecedented development of compute capability and extension of memory bandwidth on modern GPUs, parallel communication and synchronization soon becomes a major concern for continuous performance scaling. This is especially the case for ...

Harnessing the power of massively parallel devices like the graphics processing unit (GPU) is difficult for algorithms that show dynamic or inhomogeneous workloads. To achieve high performance, such advanced algorithms require scalable, concurrent ...