ASU Electronic Theses and Dissertations
- 2 English
- 2 Public
With the massive multithreading execution feature, graphics processing units (GPUs) have been widely deployed to accelerate general-purpose parallel workloads (GPGPUs). However, using GPUs to accelerate computation does not always gain good performance improvement. This is mainly due to three inefficiencies in modern GPU and system architectures. First, not all parallel threads have a uniform amount of workload to fully utilize GPU’s computation ability, leading to a sub-optimal performance problem, called warp criticality. To mitigate the degree of warp criticality, I propose a Criticality-Aware Warp Acceleration mechanism, called CAWA. CAWA predicts and accelerates the critical warp execution by allocating larger execution …
- Lee, Shin-Ying, Wu, Carole-Jean, Chakrabarti, Chaitali, et al.
- Created Date
Coarse-grained Reconfigurable Arrays (CGRAs) are promising accelerators capable of accelerating even non-parallel loops and loops with low trip-counts. One challenge in compiling for CGRAs is to manage both recurring and nonrecurring variables in the register file (RF) of the CGRA. Although prior works have managed recurring variables via rotating RF, they access the nonrecurring variables through either a global RF or from a constant memory. The former does not scale well, and the latter degrades the mapping quality. This work proposes a hardware-software codesign approach in order to manage all the variables in a local nonrotating RF. Hardware provides modulo …
- Dave, Shail, Shrivastava, Aviral, Ren, Fengbo, et al.
- Created Date