Yichen Yang

TL;DR: Prodigy as discussed by the authors is a low-cost hardware-software codesign solution for intelligent prefetching to improve the memory latency of several important irregular workloads, including graph analytics, sparse linear algebra, and fluid mechanics that exhibit two specific types of datadependent memory access patterns.

TL;DR: NDMiner is presented, a Near Data Processing (NDP) architecture that improves the performance of GPM workloads and proposes a new graph remapping scheme in memory and a hardware-based set operation reordering technique to best optimize bank, rank, and channel-level parallelism in DRAM.

TL;DR: In this paper , two strategies are employed in the framework of DDCM to improve computational efficiency: Carrera Unified Formula (CUF) is used to build the numerical model, which reduces the computational cost (amount of gauss points) and the number of iterations.

TL;DR: The analyses show that the harnessed parallelism has improved and emerging workloads show good utilization of hardware resources, and the effectiveness of software in utilizing the underlying hardware has improved, but still has scope for optimizations.

TL;DR: Graph Pattern Mining (GPM) algorithms mine structural patterns in graphs as mentioned in this paper , but the performance of GPM workloads is bottlenecked by control flow and memory stalls, this is because of data-dependent branches used in set intersection and difference operations that dominate the execution time.