As GPU's compute capabilities grow, their memory hierarchy increasingly becomes a
bottleneck. Current GPU memory hierarchies use coarse-grained memory accesses to …

While Monte Carlo Neutron Transport (MCNT) is near-embarrasingly parallel, the effectively
unpredictable lifetime of neutrons can lead to divergence when MCNT is evaluated on …

The ubiquity of graphics processing unit (GPU) architectures has made them efficient
alternatives to chip-multiprocessors for parallel workloads. GPUs achieve superior …

The ability to perform fast context-switching and massive multi-threading is the forte of
modern GPU architectures, which have emerged as an efficient alternative to traditional chip …

The purpose of this research is to find a neural-network-based solution to the well-known
problem of branch divergence in Single Instruction Multiple Data (SIMD) architectures. Our …

General purpose computing using graphics processing units (GPGPUs) is an attractive
option to achieve power efficient throughput computing. But the power efficiency of GPGPUs …

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 …

GPU's SIMD architecture is a double-edged sword confronting parallel tasks with control
flow divergence. On the one hand, it provides a high performance yet power-efficient …

Data analytics pipelines increasingly rely on databases to select, filter, and pre-process
reams of data. These databases use data structures with irregular control flow like trees and …

With the skyrocketing advances of process technology, the increased need to process huge
amount of data, and the pivotal need for power efficiency, the usage of Graphics Processing …