Julia is a high-level language that supports executing parallel code through various packages. CUDA.jl is prominently used for developing GPU Julia code across a significant number of libraries and programs. In this paper, Juliana, a new tool that translates Julia code utilizing the CUDA.jl package to an abstract multi-backend representation powered by the KernelAbstractions package, is presented. The performance impact of this translation is evaluated using a custom adaptation of the well-established Rodinia benchmark suite to Julia CUDA.jl. To ensure the viability of the tool from a performance perspective, an accurate statistical analysis of the overhead using the BenchmarkTools Julia package is performed, comparing the same benchmark code on the same CUDA device before and after the translation. Additionally, the portability of this approach is demonstrated by running the translated code across multiple backends of KernelAbstractions, allowing the execution of the Rodinia benchmark suite on different GPU vendors such as NVIDIA, Intel, AMD, or Apple.
In this paper we study the power consumption of quantum computing platforms when integrated into high-performance computing (HPC) centers. We analyze the key components of leading quantum computers (superconducting circuit, trapped ion, neutral atom and photonics), and compare their power consumption. We also introduce a quantum energy efficiency metric that combines multiple qubit performance factors and power consumption. Using this metric, we evaluate and compare the energy efficiency of various quantum computers and assess their performance.
The Edge Computing environments enable the development of pervasive applications distributed across extensive geographical areas, overcoming specific issues associated with centralized information processing, such as network bandwidth saturation and the need for large computing infrastructures. This work presents two case studies of deploying applications for environmental monitoring on low-energy and highperformance edge computing devices, employing accelerated artificial intelligence techniques based on GPUs. The first problem entails the classification of various materials within hyperspectral images, while the second problem focuses on identifying floating plastic debris. The applications were validated on a Nvidia Jetson Nano sensor board, demonstrating good accuracy, effectiveness, and energy consumption results.
Abstract. Recent wildfire events in various parts of the world (Canada, the USA, Europe, Australia, etc.) highlight that climate evolution leads to atmospheric patterns that contribute to the development of more destructive fires, revealing complex fire-atmosphere interactions. The HIDALGO2 project addresses several environmental challenges from the perspective of numerical simulation and explores the possibilities and constraints of using European HPC resources. This paper presents some of the strategies followed in the wildfire pilot study for modeling fire spread and smoke emission and dispersion. The WRF-SFIRE model allows for the prediction of atmospheric behavior and its influence on wildfire spread by coupling the factors governing both phenomena. The design of the number of domains and their resolution, as well as the parameterization for downscaling and fire spread simulation, influence performance on EuroHPC installations. Additionally, each HPC installation has particularities for its use. These aspects condition and limit their use for real-time simulations to support operational decision-making. The pre-calculation of simulation ensembles can help overcome this obstacle and also allow for landscape sensitivity analysis. Finally, some strategies for the photorealistic visualization of the results are presented.
A multi-scale modeling approach is employed for the study of the effect of oleic-acid (OA) coverage on the magnetic behaviour of Co ferrite nanoparticles (CFNs), using high performance computing (HPC). Our study is performed in three different length scales: first we perform electronic structure calculations using parallel implementation of the density functional theory (DFT) method to study the magnetic properties of ultra-small OA coated CFNs. Next taking input from the DFT data, we calculate the magnetic characteristics of larger in size OA coated CFNs at an atomic scale performing Monte Carlo simulations. Finally, a mesoscopic modeling approach for interacting assemblies of nanoparticles is employed to reduce further computation time and sources for the study of the magnetic behaviour of CFNs covered with different percentage of oleic-acid, at finite temperature. The results demonstrate that the DFT magnetic moment and magnetic anisotropy of the nanoparticle decrease with the increase of the percentage of the surfactant. However, in the assembly of CFNs the interplay between the exchange and dipolar interparticle interactions results in the increase of the magnetic anisotropy and the decrease of the saturation magnetization as the percentage of OA coverage increases, in agreement with experimental findings. The proposed multi-scale computational approach, implemented in HPC environment, illustrates its ability to handle numerical calculations on complex magnetic interactions of multiple structural components. It can overcome computational limitations to predict optimum parameters for hybrid organic/inorganic nanomaterials for various applications.
