Sparse tensor algorithms are becoming widespread, particularly in the domains of deep
learning, graph and data analytics, and scientific computing. Current high-performance …

Most of today's computer vision pipelines are built around deep neural networks, where
convolution operations require most of the generally high compute effort. The Winograd …

Simulating all threads in a scaled GPU workload results in prohibitive simulation cost. Cycle-
level simulation is orders of magnitude slower than native silicon, the only solution is to …

As GPUs continue to grow in popularity for accelerating demanding applications, such as
high-performance computing and machine learning, GPU architects need to deliver more …

Suboptimal management of memory and bandwidth is one of the primary causes of low
performance on systems comprising multiple GPUs. Existing memory management solutions …

Today, DNNs' high computational complexity and sub-optimal device utilization present a
major roadblock to democratizing DNNs. To reduce the execution time and improve device …

With the increasing demands of modern workloads, multi-GPU systems have emerged as a
scalable solution, extending performance beyond the capabilities of single GPUs. However …

GPUs, due to their massively-parallel computing architectures, provide high performance for
data-parallel applications. However, existing GPU simulators are too slow to enable …

Recent studies have shown that using fine-grained peer-to-peer (P2P) stores to
communicate among devices in multi-GPU systems is a promising path to achieve strong …

Heterogeneous architectures are vastly used in various high performance computing
systems from IoT‐based embedded architectures to edge and cloud systems. Although …