HPCASIA

Matrix-matrix multiplication is used for various linear algebra algorithms such as matrix decomposition and tensor contraction. NVIDIA Tensor Core is a mixed-precision matrix-matrix multiplication and addition computing unit, where the theoretical peak ...

In this study, we implement large integer multiplication with the Arm Scalable Vector Extension (SVE) instructions. SVE is a single instruction, multiple data (SIMD) instruction set for the Arm AArch64 architecture. We use a reduced-radix representation ...

High responsiveness is substantial for users’ satisfaction in supercomputer systems. Recently, the use of interactive jobs in addition to traditional batch jobs is attracting attention. It is getting important to handle those jobs consolidated for ...

Mixed precision Krylov solvers with the Jacobi preconditioner often show significant convergence degradation when the Jacobi preconditioner is computed in low precision such as FP16 and BF16. It is found that this convergence degradation is attributed ...

Molecular dynamics (MD) simulations exhibit big computational efforts, which makes them very time-consuming. This particularly holds for molecular-continuum simulations in fluid dynamics, which rely on the simulation of MD ensembles that are coupled to ...

Krylov subspace algorithms are important methods for solving linear systems. In order to efficiently solve large-scale linear systems, parallelism techniques are often applied. However, parallelism often enlarge the non-associativity of floating-point ...

With the emergence of new computer architectures, portability and performance-portability become significant concerns for developing HPC applications. This work reports our experience and lessons learned using DaCe to create and optimize batched ...

In High Performance Computing (HPC) systems, numerous applications of varying scale and domain are scheduled to run concurrently, and share the available CPU and memory capacities among themselves. Applications whose run-time memory usage are not known ...

Many data often come with a high volume in textual format (JSON, XML, CSV). Because parsing can easily dominate data analysis time, researchers have been working on parallelizing parsing. Operator Precedence Parsing (OPP), among candidate parsing methods,...

Exascale computing platforms with millions of compute units and with thousands of nodes are predicted to experience frequent faults which interrupt applications’ execution. In this context resilience against faults becomes important. We examine user and ...

We study software automatic tuning. Automatic tuning tools using iterative one-dimensional search estimate hyperparameters of machine learning programs. Iterative one-dimensional search searches the parameter space consisting of possible values of the ...

Recently, federated HPC and cloud resources are becoming increasingly strategic for providing diversified and geographically available computing resources. However, accessing data stores across HPC and cloud storage systems is challenging. Many cloud ...

The complementary use of graphics processing units (GPUs) and field programmable gate arrays (FPGAs) is a major topic of interest in the high-performance computing (HPC) field. GPU–FPGA-accelerated computing is an effective tool for multiphysics ...

Graph-based simultaneous localization and mapping (G-SLAM) is an intuitive SLAM implementation where graphs are used to represent poses, landmarks and sensor measurements when a mobile robot builds a map of the environment and locates itself in it. ...

FPGA clusters have yet to be a mainstream of HPC, even for accelerators, and several challenges exist in their architecture and system organization. This work presents ESSPER, a flexible and scalable FPGA cluster prototype system for reconfigurable HPC ...