Graphics Processing Units (GPUs) and CUDA

Computers almost always contain one or more central processing units (CPU), each of which processes information sequentially. While having multiple CPUs allow a computer to run several tasks in parallel, many computers also have a graphics processing unit (GPU) which contains hundreds to thousands of cores that allow it to execute many computations in parallel. In order to complete a larger task, GPUs run many subtasks concurrently. Each core performs the same instruction on different sets of data, making it useful for performing tasks such as calculating what each individual pixel displays on a screen. The purpose of this research was to learn how GPUs work, how to write CUDA programs to utilize GPUs, and to determine if GPUs could be used to increase the speed of algorithms used to determine the pebbling properties of graphs. In addition, we developed a class module on GPU computing with CUDA for the Advanced Algorithms class in Hope College’s Computer Science department

Parallelising wavefront applications on general-purpose GPU devices

Pipelined wavefront applications form a large portion of the high performance scientific computing workloads at supercomputing centres. This paper investigates the viability of graphics processing units (GPUs) for the acceleration of these codes, using NVIDIA's Compute Unified Device Architecture (CUDA). We identify the optimisations suitable for this new architecture and quantify the characteristics of those wavefront codes that are likely to experience speedups

Body of Knowledge for Graphics Processing Units (GPUs)

Graphics Processing Units (GPU) have emerged as a proven technology that enables high performance computing and parallel processing in a small form factor. GPUs enhance the traditional computer paradigm by permitting acceleration of complex mathematics and providing the capability to perform weighted calculations, such as those in artificial intelligence systems. Despite the performance enhancements provided by this type of microprocessor, there exist tradeoffs in regards to reliability and radiation susceptibility, which may impact mission success. This report provides an insight into GPU architecture and its potential applications in space and other similar markets. It also discusses reliability, qualification, and radiation considerations for testing GPUs

Numerical evaluation of multi-loop integrals

We present updates on the development of pySecDec, a toolbox to numerically evaluate parameter integrals in the context of dimensional regularization. We discuss difficulties with loop integrals in the special kinematic condition where the squared momentum of a leg is equal to the squared mass of a propagator. We further discuss some features of a Quasi Monte Carlo (QMC) integrator that can optionally run on Graphics Processing Units (GPUs).Comment: 10 pages, 5 figures, contribution to the proceedings of Loops and Legs 2018, St. Goar, German