Cloud Computing is an emerging computing paradigm. It shares massively scalable, elastic
resources (eg, data, calculations, and services) transparently among the users over a …

Algorithms operating on a graph setting are known to be highly irregular and unstructured.
This leads to workload imbalance and data locality challenge when these algorithms are …

Graph analytics has become a major workload in recent years. The underlying core
algorithms tend to be irregular and data dependent, making them challenging to parallelize …

Increasing communication overheads are already threatening computer system scaling. One
approach to dramatically reduce communication overheads is waferscale processing …

Segmented sort, as a generalization of classical sort, orders a batch of independent
segments in a whole array. Along with the wider adoption of manycore processors for HPC …

GPUs have been proven effective for structured applications that map well to the rigid 1D-3D
grid of threads in modern bulk synchronous parallel (BSP) programming languages …

Graphics processing units (GPUs) promise spectacular performance advantages when used
as database coprocessors. Their massive compute capacity, however, is often hampered by …

Recent developments in GPU execution models and architectures have introduced dynamic
parallelism to facilitate the execution of irregular applications where control flow and …

Supporting dynamic parallelism is important for GPU to benefit a broad range of
applications. There are currently two fundamental ways for programs to exploit dynamic …

Dynamic parallelism (DP) is a promising feature for GPUs, which allows on-demand
spawning of kernels on the GPU without any CPU intervention. However, this feature has …