The rapid growth in demand for HPC systems has led to a rise in carbon footprint, which
requires urgent intervention. In this work, we present a comprehensive analysis of the …

The massive parallelism present in GPUs comes at the cost of reduced L1 and L2 cache
sizes per thread, leading to serious cache contention problems such as thrashing. Hence …

Bandwidth non-uniformity in multi-chip GPUs poses a major design challenge for its last-
level cache (LLC) architecture. Whereas a memory-side LLC caches data from the local …

As GPUs are becoming widely deployed in the cloud infrastructure to support different
application domains, the security concerns of GPUs are becoming increasingly important. In …

Multi-GPU systems have emerged as a desirable platform to deliver high computing
capabilities and large memory capacity to accommodate large dataset sizes. However …

With the advancement of processor packaging technology and the looming end of Moore's
law, multi-chip-module (MCM) GPUs become a promising architecture to continue the …

This article presents a graphics processing unit (GPU) scheduling scheme that maximizes
the exploitation of data locality in deep neural networks (DNNs). Convolution is one of the …

Exploiting data locality in GPGPUs is critical for efficiently using the smaller data caches and
handling the memory bottleneck problem. This paper proposes a thread block-centric …

Multi-GPU systems have become popular to cater to the growing demands for high
parallelism and large memory capacity. However, the delivered performance is constrained …

Specialization is the key approach for continued performance growth beyond the end of
Dennard scaling. Academics and industry are hence continuously proposing new …