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Researchers within the Human Brain Project and related projects have in the last couple of years expanded their needs for high-performance computing infrastructures. The needs arise from a diverse set of science challenges that range from…
While edge computing is envisioned to superbly serve latency sensitive applications, the implementation-based studies benchmarking its performance are few and far between. To address this gap, we engineer a modular edge cloud computing…
Novel methods are presented in this initial study for the fusion of GPU kernels in the artificial compressibility method (ACM), using tensor product elements with constant Jacobians and flux reconstruction. This is made possible through the…
Configuring stream processing systems for efficient performance, especially in cloud-native deployments, is a challenging and largely manual task. We present an experiment-driven approach for automated configuration optimization that…
We ported the firmware of the ARTIQ experiment control infrastructure to an embedded system based on a commercial Xilinx Zynq-7000 system-on-chip. It contains high-performance hardwired CPU cores integrated with FPGA fabric. As with…
Implementation of basic local Monte-Carlo algorithms on ATI Graphics Processing Units (GPU) is investigated. The Ising model and pure SU(2) gluodynamics simulations are realized with the Compute Abstraction Layer (CAL) of ATI Stream…
Tensor Processing Units (TPUs) were developed by Google exclusively to support large-scale machine learning tasks. TPUs can, however, also be used to accelerate and scale up other computationally demanding tasks. In this paper we repurpose…
We have developed several autotuning benchmarks in CUDA that take into account performance-relevant source-code parameters and reach near peak-performance on various GPU architectures. We have used them during the development and evaluation…
QMCPACK has enabled cutting-edge materials research on supercomputers for over a decade. It scales nearly ideally but has low single-node efficiency due to the physics-based abstractions using array-of-structures objects, causing…
With the rapid development of scientific computation, more and more researchers and developers are committed to implementing various workloads/operations on different devices. Among all these devices, NVIDIA GPU is the most popular choice…
Today's computing systems require moving data back-and-forth between computing resources (e.g., CPUs, GPUs, accelerators) and off-chip main memory so that computation can take place on the data. Unfortunately, this data movement is a major…
Choosing an appropriate programming paradigm for high-performance computing on low-power devices can be useful to speed up calculations. Many Android devices have an integrated GPU and - although not officially supported - the OpenCL…
Recent development in lightweight OS-level virtualization, containers, provides a potential solution for running HPC applications on the cloud platform. In this work, we focus on the impact of different layers in a containerized environment…
HPC world is dominated by x86 ISA CPUs. This monoculture is not necessarily justified by best performance evaluation, but may inherit from e.g. SW related restrictions on the choice of HW platforms. To avoid running (further) into path…
Today's quantum computers are primarily accessible through the cloud and potentially shifting to the edge network in the future. With the rapid advancement and proliferation of quantum computing research worldwide, there has been a…
We introduce qclab++, a light-weight, fully-templated C++ package for GPU-accelerated quantum circuit simulations. The code offers a high degree of portability as it has no external dependencies and the GPU kernels are generated through…
We present the first comprehensive analysis of ARM MTE hardware performance on four different microarchitectures: ARM Big (A7x), Little (A5x), and Performance (Cortex-X) cores on the Google Pixel 8 and Pixel 9, and on Ampere Computing's…
Recently, a fully implicit, energy- and charge-conserving particle-in-cell method has been proposed for multi-scale, full-f kinetic simulations [G. Chen, et al., J. Comput. Phys. 230,18 (2011)]. The method employs a Jacobian-free…
The simulation of Systems-on-Chip (SoC) is nowadays a hot topic because, beyond providing many debugging facilities, it allows the development of dedicated software before the hardware is available. Low-consumption CPUs such as ARM play a…
Cycle-accurate simulators are widely used to study systolic accelerators, yet their accuracy and usability are often limited by weak validation against real hardware and poor integration with modern ML compiler stacks. This paper presents…