Many scientific applications rely on sparse direct solvers for their numerical robustness. However, performance optimization for these solvers remains a challenging task, especially on GPUs. This is due to workloads of small dense matrices that are ...

We extend the capability of space-time geostatistical modeling using algebraic approximations, illustrating application-expected accuracy worthy of double precision from majority low-precision computations and low-rank matrix approximations. We exploit ...

This article describes a standard API for a set of Batched Basic Linear Algebra Subprograms (Batched BLAS or BBLAS). The focus is on many independent BLAS operations on small matrices that are grouped together and processed by a single routine, called a ...

As the scale of high-performance computing (HPC) systems continues to grow, researchers are devoted themselves to explore increasing levels of parallelism to achieve optimal performance. The modern CPU’s design, including its features of hierarchical ...

Climate and weather can be predicted statistically via geospatial Maximum Likelihood Estimates (MLE), as an alternative to running large ensembles of forward models. The MLE-based iterative optimization procedure requires the solving of large-scale ...

Efficient processing of Irregular Matrices on Single Instruction, Multiple Data (SIMD)-type architectures is a persistent challenge. Resolving it requires innovations in the development of data formats, computational techniques, and implementations that ...

The SLATE (Software for Linear Algebra Targeting Exascale) library is being developed to provide fundamental dense linear algebra capabilities for current and upcoming distributed high-performance systems, both accelerated CPU-GPU based and CPU based. ...

This article presents an implementation of a distributed autotuning engine developed as part of the Bench-testing OpenN Software Autotuning Infrastructure project. The system is geared towards performance optimization of computational kernels for ...

Low-precision floating-point arithmetic is a powerful tool for accelerating scientific computing applications, especially those in artificial intelligence. Here, we present an investigation showing that other high-performance computing (HPC) ...

This work presents an implementation of a linear least squares solver for distributed-memory machines with GPU accelerators, developed as part of the Software for Linear Algebra Targeting Exascale (SLATE) package. From the algorithmic standpoint, the ...

We present an automated performance evaluation framework that enables an automated workflow for testing and performance evaluation of software libraries. Integrating this component into an ecosystem enables sustainable software development, as a ...

The recent version of the Parallel Linear Algebra Software for Multicore Architectures (PLASMA) library is based on tasks with dependencies from the OpenMP standard. The main functionality of the library is presented. Extensive benchmarks are targeted ...

Low-precision floating-point arithmetic is a powerful tool for accelerating scientific computing applications, especially those in artificial intelligence. Here, we present an investigation showing that other high-performance computing (HPC) ...

The increase in scale and heterogeneity of high-performance computing (HPC) systems predispose the performance of Message Passing Interface (MPI) collective communications to be susceptible to noise, and to adapt to a complex mix of hardware ...

The use of low-precision arithmetic in mixed-precision computing methods has been a powerful tool to accelerate numerous scientific computing applications. Artificial intelligence (AI) in particular has pushed this to current extremes, making use of ...

Successfully exploiting distributed collections of heterogeneous many-cores architectures with complex memory hierarchy through a portable programming model is a challenge for application developers. The literature is not short of proposals addressing ...

We propose a variety of batched routines for concurrently processing a large collection of small-size, independent sparse matrix-vector products (SpMV) on graphics processing units (GPUs). These batched SpMV kernels are designed to be flexible in order ...

Novel scalable scientific algorithms are needed in order to enable key science applications to exploit the computational power of large-scale systems. This is especially true for the current tier of leading petascale machines and the road to exascale ...

This paper presents a software framework for solving large numbers of relatively small matrix problems using GPUs. Our approach combines novel and existing HPC techniques to methodically apply performance analysis, kernel design, low-level optimizations,...

We present our performance analysis, algorithm designs, and the optimizations needed for the development of high-performance GPU-only algorithms, and in particular, for the dense Cholesky factorization. In contrast to currently promoted designs that ...