PPOPP

Matrix computations are widely used in increasing sizes and complexity in scientific computing and engineering. But current matrix language implementations lack programmer support to effectively and seamlessly utilize cloud computing resources. We ...

The increase in the number of cores available in modern processors makes it important for implementations to maximize their use within a node by overlapping communication and computation. However, when the dependencies between communication and ...

This work proposes a novel algorithm and Integer Linear Programming (ILP) formulation to optimize the pipelined code mapping of dataflow graph under a given budget generated by optimizing compilers. The goal of this optimization technique is to ...

Semi-automatic parallelization provides abstractions that simplify the programming effort and allow the user to make decisions that cannot be made by tools. However, abstractions for general-purpose systems usually do not carry sufficient knowledge ...

We introduce Harmonic CUDA, a dataflow programming model for GPUs that allows programmers to describe algorithms as a dependency graph of producers and consumers where data flows continuously through the graph for the duration of the kernel. This ...

MPI+X is the most popular hybrid programming model for distributed computation on modern heterogeneous HPC systems. Nonetheless, for simplicity, HPC developers ideally would like to implement multi-node distributed parallel computing through a single ...

Computing on heterogeneous architecture involving CPUs and accelerators is now a popular choice of parallel computing. As a directive-based programming model, OpenMP has become more and more comprehensive that supports a large variety of hardware ...