Related papers: HEPScore: A new CPU benchmark for the WLCG
The recent years witness a trend of applying large-scale distributed deep learning in both business and scientific computing areas, whose goal is to speed up the training time to achieve a state-of-the-art quality. The HPC community feels a…
Recent innovations focused around {\em parallel} processing, either through systems containing multiple processors or processors containing multiple cores, hold great promise for enhancing the performance of the trigger at the LHC and…
In this paper we present a new accounting model for heterogeneous supercomputers. An increasing number of supercomputing centres adopt heterogeneous architectures consisting of CPUs and hardware accelerators for their systems. Accounting…
Robustly estimating energy consumption in High-Performance Computing (HPC) is essential for assessing the energy footprint of modern workloads, particularly in fields such as Artificial Intelligence (AI) research, development, and…
The 5Gperf project was conducted by Huawei research teams in 2016-17. It was concerned with the acceleration of signal-processing algorithms for a 5G base-station prototype. It improved on already optimized SIMD-parallel CPU algorithms and…
A new class of Second generation high-performance computing applications with heterogeneous, dynamic and data-intensive properties have an extended set of requirements, which cover application deployment, resource allocation, -control, and…
Technological advances in the past decade, hardware and software alike, have made access to high-performance computing (HPC) easier than ever. We review these advances from a statistical computing perspective. Cloud computing makes access…
The aim of this article is to present an overview of the major families of state-of-the-art data processing benchmarks, namely transaction processing benchmarks and decision support benchmarks. We also address the newer trends in cloud…
Recent trends in deep learning (DL) have made hardware accelerators essential for various high-performance computing (HPC) applications, including image classification, computer vision, and speech recognition. This survey summarizes and…
In November 2022, the HEP Software Foundation and the Institute for Research and Innovation for Software in High-Energy Physics organized a workshop on the topic of Software Citation and Recognition in HEP. The goal of the workshop was to…
HEP-Frame is a new C++ package designed to efficiently perform analyses of data sets from a very large number of events, like those available at the Large Hadron Collider (LHC) at CERN, Geneva. It mainly targets high performance servers and…
Heterogeneous embedded systems on chip (HESoCs) co-integrate a standard host processor with programmable manycore accelerators (PMCAs) to combine general-purpose computing with domain-specific, efficient processing capabilities. While…
HEP Cluster is designed and implemented in Scientific Linux Cern 5.5 to grant High Energy Physics researchers one place where they can go to undertake a particular task or to provide a parallel processing architecture in which CPU resources…
High-level synthesis (HLS) tools have brought FPGA development into the mainstream, by allowing programmers to design architectures using familiar languages such as C, C++, and OpenCL. While the move to these languages has brought…
Real-time remote sensing applications like search and rescue missions, military target detection, environmental monitoring, hazard prevention and other time-critical applications require onboard real time processing capabilities or…
Can cloud computing infrastructures provide HPC-competitive performance for scientific applications broadly? Despite prolific related literature, this question remains open. Answers are crucial for designing future systems and democratizing…
Hierarchical Text Classification (HTC) is a natural language processing task with the objective to classify text documents into a set of classes from a structured class hierarchy. Many HTC approaches have been proposed which attempt to…
During the last two years, the goal of many researchers has been to squeeze the last bit of performance out of HPC system for AI tasks. Often this discussion is held in the context of how fast ResNet50 can be trained. Unfortunately,…
The ability to understand how a scientific application is executed on a large HPC system is of great importance in allocating resources within the HPC data center. In this paper, we describe how we used system performance data to identify:…
Next-generation supercomputers will feature more hierarchical and heterogeneous memory systems with different memory technologies working side-by-side. A critical question is whether at large scale existing HPC applications and emerging…