Load balancing techniques (eg work stealing) are important to obtain the best performance
for distributed task scheduling systems that have multiple schedulers making scheduling …

State-of-the-art, yet decades-old, architecture of high-performance computing systems has
its compute and storage resources separated. It thus is limited for modern data-intensive …

Resource and job management software is crucial to High Performance Computing (HPC)
for efficient application execution. However, current systems and approaches can no longer …

This paper introduces PapyrusKV, a parallel embedded key-value store (KVS) for distributed
high-performance computing (HPC) architectures that offer potentially massive pools of …

In the Big Data era, the gap between the storage performance and an application's I/O
requirement is increasing. I/O congestion caused by concurrent storage accesses from …

Data‐driven programming models such as many‐task computing (MTC) have been
prevalent for running data‐intensive scientific applications. MTC applies over …

With the exponential growth of supercomputers in parallelism, applications are growing
more diverse, including traditional large-scale HPC MPI jobs, and ensemble workloads such …

Data driven programming models like MapReduce have gained the popularity in large-scale
data processing. Although great efforts through the Hadoop implementation and framework …

One way to efficiently utilize the coming exascale machines is to support a mixture of
applications in various domains, such as traditional large-scale HPC, the ensemble runs …

Owing to the extreme parallelism and the high component failure rates of tomorrow's
exascale, high-performance computing (HPC) system software will need to be scalable …