Xulong Tang

TL;DR: Two new runtime techniques are developed: a regression-based affinity prediction model and mechanism that accurately identifies which kernels would benefit from PIM and offloads them to GPU cores in memory, and a concurrent kernel management mechanism that uses the affinity Prediction model, a new kernel execution time prediction model, and kernel dependency information to decide which kernels to schedule concurrently on main GPU cores and the GPU core in memory.

TL;DR: This work proposes YOLObile framework, a real-time object detection on mobile devices via compression-compilation co-design, and proposes a novel block-punched pruning scheme for any kernel size.

TL;DR: This work proposes SPAWN, a runtime framework that controls the dynamically-generated kernels, thereby directly reducing the associated launch overheads and queuing latency and achieves 69% and 57% speedup over the flat (non-DP) implementation and baseline DP, respectively.

TL;DR: The potential of compiler support in exploiting NDP in the context of emerging many core systems is explored and a novel compiler algorithm that partitions the computations in a given loop nest into sub computations and schedules the resulting sub computation on different cores with the goal of reducing the distance-to-data on the on-chip network is proposed.

TL;DR: This paper develops two offloading techniques, called LLC-Compute and Omni- Compute, which employ simple bookkeeping hardware to enable GPU cores to compute instructions offloaded by other GPU cores.