Data prefetching, ie, the act of predicting an application's future memory accesses and
fetching those that are not in the on-chip caches, is a well-known and widely used approach …

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 …

Human brain network analysis based on machine learning has been paid much attention in
the field of neuroimaging, where the application of convolutional neural network (CNN) is …

We are entering an era of “killer microseconds” in data center applications. Killer
microseconds refer to μs-scale “holes” in CPU schedules caused by stalls to access fast I/O …

The Register File (RF) in GPUs is a critical structure that maintains the state for thousands of
threads that support the GPU processing model. The RF organization substantially affects …

Multi-application execution in Graphics Processing Units (GPUs), a promising way to utilize
GPU resources, is still challenging. Some pieces of prior work (eg, spatial multitasking) have …

Dynamic neural networks enable higher representation flexibility compared to networks with
a fixed architecture and are extensively deployed in problems dealing with varying input …

Graphics Processing Units (GPUs) are widely used as the accelerator of choice for
applications with massively data-parallel tasks. However, recent studies show that GPUs …

A generally used GPU programming methodology is that adjacent threads access data in
neighbor or specific-stride memory addresses and perform computations with the fetched …

As modern GPU workloads grow in size and complexity, there is an ever-increasing demand
for GPU computational power. Emerging workloads contain hundreds or thousands of GPU …