A very common problem in GPU programming is that some combination of thread block
dimensions and other code optimization parameters, like tiling or unrolling factors, results in …

Contemporary graphics processing units (GPUs) support dynamic voltage and frequency
scaling to balance computational performance and energy consumption. However, accurate …

Finding optimal parameter configurations for tunable GPU kernels is a non-trivial exercise
for large search spaces, even when automated. This poses an optimization task on a …

Recent years have witnessed phenomenal growth in the application, and capabilities of
graphical processing units (GPUs) due to their high parallel computation power at relatively …

Graphics Processing Units (GPUs) have revolutionized the computing landscape over the
past decade. However, the growing energy demands of data centres and computing …

The computational patterns of stencil operations are commonly used in HPC applications.
Many HPC platforms utilize the computation capability of GPUs to accelerate stencil …

The effectiveness of Machine Learning (ML) methods depend on access to large suitable
datasets. In this article, we present how we build the LS-CAT (Large-Scale CUDA …

Stencil computations are widely used in high performance computing (HPC) applications.
Many HPC platforms utilize the high computation capability of GPUs to accelerate stencil …

This article proposes the Coding Ants framework, an approach for auto-tuning which uses
ant colony optimization to find a sequence of code optimizations for GPU architectures. The …

GPU kernels have come to the forefront of comput-ing due to their utility in varied fields, from
high-performance computing to machine learning. A typical GPU compute kernel is invoked …