Related papers: RNTuple performance: Status and Outlook

High performance computing (HPC) applications have a high requirement on storage speed and capacity. Non-volatile memory is a promising technology to replace traditional storage devices to improve HPC performance. Earlier in 2017, Intel and…

With the increasing usage of Machine Learning (ML) in High energy physics (HEP), there is a variety of new analyses with a large spread in compute resource requirements, especially when it comes to GPU resources. For institutes, like the…

Multi-Layer Perceptron (MLP) models are the foundation of contemporary point cloud processing. However, their complex network architectures obscure the source of their strength and limit the application of these models. In this article, we…

Structural pruning can simplify network architecture and improve inference speed. We propose Hardware-Aware Latency Pruning (HALP) that formulates structural pruning as a global resource allocation optimization problem, aiming at maximizing…

Abstract. Most of the real world data we encounter are asynchronous event sequence, so the last decades have been characterized by the implementation of various point process into the field of social networks,electronic medical records and…

Heterogeneous systems have become one of the most common architectures today, thanks to their excellent performance and energy consumption. However, due to their heterogeneity they are very complex to program and even more to achieve…

These days MANET is an amazing remarkably altering or rising technology, for the reason that of its elite nature of scattered mobile devices and self motivated network topology. The mobile adhoc routing protocol follows several principles…

Hybrid parallelism underpins large-scale LLM training across tens of thousands of GPUs. At such scale, hardware failures on individual devices lead to performance skew across devices, diminishing overall training efficiency. Existing…

The ROOT based Offline and Online Analysis (ROAn) framework was developed to perform data analysis on data from Depleted P-channel Field Effect Transistor (DePFET) detectors, a type of active pixel sensors developed at the MPI…

Recent years have seen the development and growth of machine learning in high energy physics. There will be more effort to continue exploring its full potential. To make it easier for researchers to apply existing algorithms and neural…

In April 2023, HEPScore23, the new benchmark based on HEP specific applications, was adopted by WLCG, replacing HEP-SPEC06. As part of the transition to the new benchmark, the CPU corepower published by the sites needed to be compared with…

Cloud computing has attracted both end-users and Cloud Service Providers (CSPs) in recent years. Improving resource utilization rate (RUtR), such as CPU and memory usages on servers, while maintaining Quality-of-Service (QoS) is one key…

Retrieval-augmented large language models (R-LLMs) combine pre-trained large language models (LLMs) with information retrieval systems to improve the accuracy of factual question-answering. However, current libraries for building R-LLMs…

The ability to record and replay program executions with low overhead enables many applications, such as reverse-execution debugging, debugging of hard-to-reproduce test failures, and "black box" forensic analysis of failures in deployed…

Recognizing Textual Entailment (RTE) was proposed as a unified evaluation framework to compare semantic understanding of different NLP systems. In this survey paper, we provide an overview of different approaches for evaluating and…

The growth and evolution of Internet of Things (IoT) is now of paramount importance for next-generation networks, including the upcoming 6G. In particular, there is a set of constrained IoT nodes that comprise the Low-Power and Lossy…

Detector studies for future experiments rely on advanced software tools to estimate performance and optimize their design and technology choices. The Key4hep project provides a flexible turnkey solution for the full experiment life-cycle…

Large High Energy Physics (HEP) experiments adopted a distributed computing model more than a decade ago. WLCG, the global computing infrastructure for LHC, in partnership with the US Open Science Grid, has achieved data management at the…

Statistical modeling is a key element in many scientific fields and especially in High-Energy Physics (HEP) analysis. The standard framework to perform this task in HEP is the C++ ROOT/RooFit toolkit; with Python bindings that are only…

Large Language Models (LLMs) have revolutionized AI applications, but deploying them at scale presents significant challenges. We present RTP-LLM, a high-performance inference engine for industrial-scale LLM deployment, successfully…