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As high-performance computing (HPC) systems rapidly evolve, with increasing on-node parallelism and widespread use of accelerators, understanding how the code maps to hardware is essential for reaching optimal performance. Benchmarks are…
Ookami is a computer technology testbed supported by the United States National Science Foundation. It provides researchers with access to the A64FX processor developed by Fujitsu in collaboration with RIK{\Xi}N for the Japanese path to…
Many important computational problems require utilization of high performance computing (HPC) systems that consist of multi-level structures combining higher and higher numbers of devices with various characteristics. Utilizing full power…
Heterogeneous computing can potentially offer significant performance and performance per watt improvements over homogeneous computing, but the question "what is the ideal mapping of algorithms to architectures?" remains an open one. In the…
As exascale systems reach unprecedented concurrency, traditional performance analysis tools struggle with the overhead of massive-scale telemetry. We present an accelerated infrastructure for the hpcanalysis framework that leverages a…
Benchmarking of CPU resources in WLCG has been based on the HEP-SPEC06 (HS06) suite for over a decade. It has recently become clear that HS06, which is based on real applications from non-HEP domains, no longer describes typical HEP…
The ongoing hardware evolution exhibits an escalation in the number, as well as in the heterogeneity, of computing resources. The pressure to maintain reasonable levels of performance and portability forces application developers to leave…
Minimizing the energy consumption of Linux-based devices is an essential step towards their wide deployment in various IoT scenarios. Energy saving methods such as duty-cycling aim to address this constraint by limiting the amount of time…
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 A64FX CPU is arguably the most powerful Arm-based processor design to date. Although it is a traditional cache-based multicore processor, its peak performance and memory bandwidth rival accelerator devices. A good understanding of its…
We present a lightweight tool for the analysis and tuning of application data placement in systems with heterogeneous memory pools. The tool allows non-intrusively identifying, analyzing, and controlling the placement of individual…
The Epiphany is a many-core, low power, low on-chip memory architecture and one can very cheaply gain access to a number of parallel cores which is beneficial for HPC education and prototyping. The very low power nature of these…
FPGAs are an attractive type of accelerator for all-purpose HPC computing systems due to the possibility of deploying tailored hardware on demand. However, the common tools for programming and operating FPGAs are still complex to use,…
The Alps Research Infrastructure leverages GH200 technology at scale, featuring 10,752 GPUs. Accessing Alps provides a significant computational advantage for researchers in Artificial Intelligence (AI) and Machine Learning (ML). While Alps…
Virtual Trusted Platform modules (TPMs) were proposed as a software-based alternative to the hardware-based TPMs to allow the use of their cryptographic functionalities in scenarios where multiple TPMs are required in a single platform,…
With the growing demands of consumer electronic products, the computational requirements are increasing exponentially. Due to the applications' computational needs, the computer architects are trying to pack as many cores as possible on a…
Today, using multiple heterogeneous accelerators efficiently from applications and high-level frameworks, such as TensorFlow and Caffe, poses significant challenges in three respects: (a) sharing accelerators, (b) allocating available…
Graphics Processing Units (GPUs) consisting of Streaming Multiprocessors (SMs) achieve high throughput by running a large number of threads and context switching among them to hide execution latencies. The number of thread blocks, and hence…
Amid the rapid advancements in large machine learning (ML) models, universities worldwide are investing substantial funds and efforts into GPU clusters. However, managing a shared GPU cluster poses a pyramid of challenges, from hardware…
For scientific software, especially those used for large-scale simulations, achieving good performance and efficiently using the available hardware resources is essential. It is important to regularly perform benchmarks to ensure the…