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In this paper we explore the performance of Intel Xeon MAX CPU Series, representing the most significant new variation upon the classical CPU architecture since the Intel Xeon Phi Processor. Given the availability of a large on-package…
Networks-on-Chips (NoCs) recently became widely used, from multi-core CPUs to edge-AI accelerators. Emulation on FPGAs promises to accelerate their RTL modeling compared to slow simulations. However, realistic test stimuli are challenging…
We present an expanded study of the performance of FLASH when using Linux Kernel Hugepages on Ookami, an HPE Apollo 80 A64FX platform. FLASH is a multi-scale, multi-physics simulation code written principally in modern Fortran and makes use…
Machine learning applications that are implemented with spike-based computation model, e.g., Spiking Neural Network (SNN), have a great potential to lower the energy consumption when they are executed on a neuromorphic hardware. However,…
Scientific simulation leveraging high-performance computing (HPC) systems is crucial for modeling complex systems and phenomena in fields such as astrophysics, climate science, and fluid dynamics, generating massive datasets that often…
We present the newly-incorporated gray radiation hydrodynamics capabilities of the FLASH code based on a radiation flux-limiter aware hydrodynamics numerical implementation designed specifically for applications in astrophysical problems.…
With at least 50 cores, Intel Xeon Phi is a true many-core architecture. Featuring fairly powerful cores, two cache levels, and very fast interconnections, the Xeon Phi can get a theoretical peak of 1000 GFLOPs and over 240 GB/s. These…
This paper reports our efforts on swCaffe, a highly efficient parallel framework for accelerating deep neural networks (DNNs) training on Sunway TaihuLight, the current fastest supercomputer in the world that adopts a unique many-core…
In the push for exascale computing, energy efficiency is of utmost concern. System architectures often adopt accelerators to hasten application execution at the cost of power. The Intel Xeon Phi co-processor is unique accelerator that…
Researchers within the Human Brain Project and related projects have in the last couple of years expanded their needs for high-performance computing infrastructures. The needs arise from a diverse set of science challenges that range from…
The Cerebras Wafer Scale Engine (WSE) is an accelerator that combines hundreds of thousands of AI-cores onto a single chip. Whilst this technology has been designed for machine learning workloads, the significant amount of available raw…
The paper demonstrates the optimization of the execution environment of a hybrid OpenMP+MPI computational fluid dynamics code (shallow water equation solver) on a cluster enabled with Intel Xeon Phi coprocessors. The discussion includes:…
The development of the A64FX processor by Fujitsu has created a massive innovation in High-Performance Computing and the birth of Fugaku: the current world's fastest supercomputer. A variety of tools are used to analyze the run-times and…
We present efforts at improving the performance of FLASH, a multi-scale, multi-physics simulation code principally for astrophysical applications, by using huge pages on Ookami, an HPE Apollo 80 A64FX platform. FLASH is written principally…
For decades, supercritical flame simulations incorporating detailed chemistry and real-fluid transport have been limited to millions of cells, constraining the resolved spatial and temporal scales of the physical system. We optimize the…
Today, many scientific and engineering areas require high performance computing to perform computationally intensive experiments. For example, many advances in transport phenomena, thermodynamics, material properties, computational…
Phase equilibrium calculations are an essential part of numerical simulations of multi-component multi-phase flow in porous media, accounting for the largest share of the computational time. In this work, we introduce a GPUenabled, fast,…
Edge AI applications increasingly require ultra-low-power, low-latency inference. Neuromorphic computing based on event-driven spiking neural networks (SNNs) offers an attractive path, but practical deployment on resource-constrained…
Recently, both industry and academia have proposed several different neuromorphic systems to execute machine learning applications that are designed using Spiking Neural Networks (SNNs). With the growing complexity on design and technology…
Spiking Neural Networks (SNNs) have the potential to drastically reduce the energy requirements of AI systems. However, mainstream accelerators like GPUs and TPUs are designed for the high arithmetic intensity of standard ANNs so are not…