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Related papers: Progress and status of APEmille

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APEmille is a SIMD parallel processor under development at the Italian National Institute for Nuclear Physics (INFN). APEmille is very well suited for Lattice QCD applications, both for its hardware characteristics and for its software and…

High Energy Physics - Lattice · Physics 2009-10-28 Emanuele Panizzi

We present the APE (Array Processor Experiment) project for the development of dedicated parallel computers for numerical simulations in lattice gauge theories. While APEmille is a production machine in today's physics simulations at…

This paper presents the status of the APEmille project, which is essentially completed, as far as machine development and construction is concerned. Several large installations of APEmille are in use for physics production runs leading to…

We present the current status of the apeNEXT project. Aim of this project is the development of the next generation of APE machines which will provide multi-teraflop computing power. Like previous machines, apeNEXT is based on a custom…

Parallel computers dedicated to lattice field theories are reviewed with emphasis on the three recent projects, the Teraflops project in the US, the CP-PACS project in Japan and the 0.5-Teraflops project in the US. Some new commercial…

High Energy Physics - Lattice · Physics 2009-10-22 Y. Iwasaki

We present the current status of the apeNEXT project. Aim of this project is the development of the next generation of APE machines which will provide multi-teraflop computing power. Like previous machines, apeNEXT is based on a custom…

APENEXT is a new generation APE processor, optimized for LGT simulations. The project follows the basic ideas of previous APE machines and develops simple and cheap parallel systems with multi T-Flops processing power. This paper describes…

This paper is a slightly modified and reduced version of the proposal of the {\bf apeNEXT} project, which was submitted to DESY and INFN in spring 2000. .It presents the basic motivations and ideas of a next generation lattice QCD (LQCD)…

High Energy Physics - Lattice · Physics 2012-08-27 The APE Collaboration

Many scientific computations need multi-node parallelism for matching up both space (memory) and time (speed) ever-increasing requirements. The use of GPUs as accelerators introduces yet another level of complexity for the programmer and…

SSE (streaming SIMD extensions) and AVX (advanced vector extensions) are SIMD (single instruction multiple data streams) instruction sets supported by recent CPUs manufactured in Intel and AMD. This SIMD programming allows parallel…

High Energy Physics - Lattice · Physics 2013-11-05 Hwancheol Jeong , Sunghoon Kim , Weonjong Lee , Seok-Ho Myung

We discuss the state of art of Lattice Boltzmann (LB) computing, with special focus on prospective LB schemes capable of meeting the forthcoming Exascale challenge. After reviewing the basic notions of LB computing, we discuss current…

Computational Physics · Physics 2020-06-14 Sauro Succi , Giorgio Amati , Massimo Bernaschi , Giacomo Falcucci , Marco Lauricella , Andrea Montessori

Two factors, which affect simulation quality are the amount of computing power and implementation. The Streaming SIMD (single instruction multiple data) extensions (SSE) present a technique for influencing both by exploiting the processor's…

Computational Engineering, Finance, and Science · Computer Science 2013-09-04 Shyam Srinivasan

The current status of United States projects pursuing Teraflops-scale computing resources for lattice field theory is discussed. Two projects are in existence at this time: the Multidisciplinary Teraflops Project, incorporating the…

High Energy Physics - Lattice · Physics 2009-10-28 Robert D. Mawhinney

Future applications demand more performance, but technology advances have been faltering. A promising approach to further improve computer system performance under energy constraints is to employ hardware accelerators. Already today, mobile…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-11-25 Mark D. Hill , Vijay Janapa Reddi

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…

Deep neural networks with large model sizes achieve state-of-the-art results for tasks in computer vision (CV) and natural language processing (NLP). However, these large-scale models are too compute- or memory-intensive for…

Distributed, Parallel, and Cluster Computing · Computer Science 2021-10-29 Yang Hu , Connor Imes , Xuanang Zhao , Souvik Kundu , Peter A. Beerel , Stephen P. Crago , John Paul N. Walters

We review the architecture of massively parallel machines used for lattice QCD simulations and present benchmarks for the performance of popular algorithms on these platforms. We cover commercial supercomputers, PC clusters, and…

High Energy Physics - Lattice · Physics 2016-09-01 Tilo Wettig

We present a parallel FFT algorithm for SIMD systems following the `Transpose Algorithm' approach. The method is based on the assignment of the data field onto a 1-dimensional ring of systolic cells. The systolic array can be universally…

High Energy Physics - Lattice · Physics 2015-06-25 Thomas Lippert , Klaus Schilling , Federico Toschi , Sven Trentmann , Raffaele Tripiccione

Large language models (LLMs) have emerged as a powerful foundation for intelligent reasoning and decision-making, demonstrating substantial impact across a wide range of domains and applications. However, their massive parameter scales and…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-12-29 Mingyu Sun , Xiao Zhang , Shen Qu , Yan Li , Mengbai Xiao , Yuan Yuan , Dongxiao Yu

To achieve high performance on modern computers, it is vital to map algorithmic parallelism to that inherent in the hardware. From an application developer's perspective, it is also important that code can be maintained in a portable manner…

Distributed, Parallel, and Cluster Computing · Computer Science 2016-06-20 Alan Gray , Kevin Stratford
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