Sparse general matrix-matrix multiplication (SpGEMM) is one of the most fundamental
building blocks in sparse linear solvers, graph processing frameworks and machine learning …

Sparse general matrix-matrix multiplication (SpGEMM) is an important kernel in
computational science and engineering, and has been widely studied on homogeneous …

Algebraic multigrid (AMG) methods are particularly efficient to solve a wide range of sparse
linear systems, due to their good flexibility and adaptability. Even though modern parallel …

As the core density of future processors keeps increasing, MPI+ Threads is becoming a
promising programming model for large scale SMP clusters. Generally speaking, hybrid …

In modern large-scale supercomputing applications, algebraic multigrid (AMG) is a leading
choice for solving matrix equations. However, the high cost of communication relative to that …

Performance of scientific codes is increasingly dependent on the input problem, its data
representation and the underlying hardware with the increase in code and architectural …

Half-precision hardware support is now almost ubiquitous. In contrast to its active use in AI,
half-precision is less commonly employed in scientific and engineering computing. The …

With the growing number of GPU-based supercomputing platforms and GPU-enabled
applications, the ability to accurately model the performance of such applications is …

The solution of large-scale combustion problems with codes such as the Arches component
of Uintah on next generation computer architectures requires the use of a many and multi …

The solution of large-scale combustion problems with codes such as Uintah on modern
computer architectures requires the use of multithreading and GPUs to achieve …