Related papers: Deploying a Top-100 Supercomputer for Large Parall…
In this report, I describe the design and implementation of an inexpensive, eight node, 32 core, cluster of raspberry pi single board computers, as well as the performance of this cluster on two computational tasks, one that requires…
The objective of our research is to demonstrate the practical usage and orders of magnitude speedup of real-world applications by using alternative technologies to support high performance computing. Currently, the main barrier to the…
This paper presents a methodology for simultaneous heterogeneous computing, named ENEAC, where a quad core ARM Cortex-A53 CPU works in tandem with a preprogrammed on-board FPGA accelerator. A heterogeneous scheduler distributes the tasks…
The growing demand for computational power is driven by advancements in deep learning, the increasing need for big data processing, and the requirements of scientific simulations for academic and research purposes. Developing countries like…
Intensive computation is entering data centers with multiple workloads of deep learning. To balance the compute efficiency, performance, and total cost of ownership (TCO), the use of a field-programmable gate array (FPGA) with…
Otus is a high-performance computing cluster that was launched in 2025 and is operated by the Paderborn Center for Parallel Computing (PC2) at Paderborn University in Germany. The system is part of the National High Performance Computing…
Training and deploying deep learning models in real-world applications require processing large amounts of data. This is a challenging task when the amount of data grows to a hundred terabytes, or even, petabyte-scale. We introduce a hybrid…
The Aurora supercomputer, which was deployed at Argonne National Laboratory in 2024, is currently one of three Exascale machines in the world on the Top500 list. The Aurora system is composed of over ten thousand nodes each of which…
The aggregate power use of computing hardware is an important cost factor in scientific cluster and distributed computing systems. The Worldwide LHC Computing Grid (WLCG) is a major example of such a distributed computing system, used…
We outline the essential features of a Linux PC cluster which is now being developed at National Taiwan University, and discuss how to optimize its hardware and software for lattice QCD with overlap Dirac quarks. At present, the cluster…
Deep convolutional neural networks (CNNs) obtain outstanding results in tasks that require human-level understanding of data, like image or speech recognition. However, their computational load is significant, motivating the development of…
The accelerating technological landscape and drive towards net-zero emission made the power system grow in scale and complexity. Serial computational approaches for grid planning and operation struggle to execute necessary calculations…
The first stage in the construction of the 0.8 Teraflops Supercomputer at Columbia, a working, two node parallel computer, has been successfully completed. The next stage, a 512 node, 26 Gigaflops prototype, is in its final construction…
The rise of power-efficient embedded computers based on highly-parallel accelerators opens a number of opportunities and challenges for researchers and engineers, and paved the way to the era of edge computing. At the same time, advances in…
Heterogeneous supercomputers have become the standard in HPC. GPUs in particular have dominated the accelerator landscape, offering unprecedented performance in parallel workloads and unlocking new possibilities in fields like AI and…
We present the outline of a research project aimed at designing and constructing a hybrid computing system that can be easily scaled up to petaflops speeds. As a first step, we envision building a prototype which will consist of three main…
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…
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…
Parallel computing using accelerators has gained widespread research attention in the past few years. In particular, using GPUs for general purpose computing has brought forth several success stories with respect to time taken, cost, power,…
The tremendous advance in computer technology in the past decade has made it possible to achieve the performance of a supercomputer on a very small budget. We have built a multi-CPU cluster of Pentium PC capable of parallel computations…