The cost of data movement has always been an important concern in high performance
computing (HPC) systems. It has now become the dominant factor in terms of both energy …

Exascale systems will require new approaches to performance observation, analysis, and
runtime decision-making to optimize for performance and efficiency. The standard" first …

The sparse matrix–vector multiply (SpMV) operation is a key computational kernel in many
simulations and linear solvers. The large communication requirements associated with a …

The Resource and Job Management System (RJMS) is a crucial system software part of the
HPC stack. It is responsible for efficiently delivering computing power to applications in …

Distributing a simulation across many machines can drastically speed up computations and
increase detail. The computing cloud provides tremendous computing resources, but weak …

Process map** (or process placement) is a useful algorithmic technique to optimize the
way applications are launched and executed onto a parallel machine. By taking into account …

The evolution of massively parallel supercomputers make palpable two issues in particular:
the load imbalance and the poor management of data locality in applications. Thus, with the …

Graph partitioning and repartitioning have been widely used by scientists to parallelize
compute-and dataintensive simulations. However, existing graph (re) partitioning algorithms …

The scalability of high-performance, parallel iterative applications is directly affected by how
well they use the available computing resources. These applications are subject to load …

MPI process placement is an important step to achieve scalable performance on modern
non-uniform memory access (NUMA) systems. A recent study on NUMA architectures has …