Related papers: Acceleration-as-a-Service: Exploiting Virtualised …

We show that numerical computations based on tensor renormalization group (TRG) methods can be significantly accelerated with PyTorch on graphics processing units (GPUs) by leveraging NVIDIA's Compute Unified Device Architecture (CUDA). We…

Due to the surge in the volume of data generated and rapid advancement in Artificial Intelligence (AI) techniques like machine learning and deep learning, the existing traditional computing models have become inadequate to process an…

With the advent of AWS Lambda in 2014, Serverless Computing, particularly Function-as-a-Service (FaaS), has witnessed growing popularity across various application domains. FaaS enables an application to be decomposed into fine-grained…

Hardware accelerators (such as Nvidia's CUDA GPUs) have tremendous promise for computational science, because they can deliver large gains in performance at relatively low cost. In this work, we focus on the use of Nvidia's Tesla GPU for…

In this short review we present the developments over the last 5 decades that have led to the use of Graphics Processing Units (GPUs) for astrophysical simulations. Since the introduction of NVIDIA's Compute Unified Device Architecture…

With the rapid development of information transmission, Software as a Service (SaaS) is developing at a rapid speed that everything originally local tends to be transplanted onto servers and executed on the cloud. WebGPU is such a SaaS…

The strategy of using CUDA-compatible GPUs as a parallel computation solution to improve the performance of programs has been more and more widely approved during the last two years since the CUDA platform was released. Its benefit extends…

GPUs are readily available in cloud computing and personal devices, but their use for data processing acceleration has been slowed down by their limited integration with common programming languages such as Python or Java. Moreover, using…

This dissertation presents the design, implementation and evaluation of GPU-accelerated simulation frameworks for Evolutionary Spatial Cyclic Games (ESCGs), a class of agent-based models used to study ecological and evolutionary dynamics.…

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…

Numerical integration of stochastic differential equations is commonly used in many branches of science. In this paper we present how to accelerate this kind of numerical calculations with popular NVIDIA Graphics Processing Units using the…

Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) bring unprecedented performance requirements for automotive systems. Graphic Processing Unit (GPU) based platforms have been deployed with the aim of meeting these…

The modern trend in High-Performance Computing (HPC) involves the use of accelerators such as Graphics Processing Units (GPUs) alongside Central Processing Units (CPUs) to speed up numerical operations in various applications. Leading…

Infrastructure as a Service (IaaS) Cloud services allow users to deploy distributed applications in a virtualized environment without having to customize their applications to a specific Platform as a Service (PaaS) stack. It is common…

This work presents an updated and extended guide on methods of a proper acceleration of the Monte Carlo integration of stochastic differential equations with the commonly available NVIDIA Graphics Processing Units using the CUDA programming…

We describe the GPU implementation of shifted or multimass iterative solvers for sparse linear systems of the sort encountered in lattice gauge theory. We provide a generic tool that can be used by those without GPU programming experience…

Cloud computing has transformed the way organizations manage and scale their IT infrastructure by offering flexible, scalable, and cost-effective solutions. However, the Infrastructure as a Service (IaaS) model faces performance challenges…

High-performance computing has recently seen a surge of interest in heterogeneous systems, with an emphasis on modern Graphics Processing Units (GPUs). These devices offer tremendous potential for performance and efficiency in important…

GPUs in High-Performance Computing systems remain under-utilised due to the unavailability of schedulers that can safely schedule multiple applications to share the same GPU. The research reported in this paper is motivated to improve the…

The rapid adoption of AI and convenience offered by cloud services have resulted in the growing demands for GPUs in the cloud. Generally, GPUs are physically attached to host servers as PCIe devices. However, the fixed assembly combination…