New GPU Standard PCIe Card Holder Design

The technique disclosed conveniently delivers a holder for GPU computing cards or any standard PCIe form factor card within a computer system. To ensure the computer system can fix in place a GPU computing card or any PCIe form factor card installed, a holder to fix the card within the chassis enclosure to resist the shock and vibration is an effective solution. Artificial Intelligence (AI) and Machine Learning (ML) based computer systems may require multiple embedded GPU computing cards. In such examples, this new idea can provide a solid holder to fix the GPU computing cards, as well as provide effective use of the limited space inside the computer system. The key feature of the present invention is to provide flexible mounting orientations and to fix the GPU computing card or any PCIe form factor card inside a computer system

GPU Computing Taxonomy

Over the past few years, a number of efforts have been made to obtain benefits from graphic processing unit (GPU) devices by using them in parallel computing. The main advantage of GPU computing is that it provides cheap parallel processing environments for those who need to solve single program multiple data (SPMD) problems. In this chapter, a GPU computing taxonomy is proposed for classifying GPU computing into four different classes depending on different strategies of combining CPUs and GPUs

A GPU-Computing Approach to Solar Stokes Profile Inversion

We present a new computational approach to the inversion of solar photospheric Stokes polarization profiles, under the Milne-Eddington model, for vector magnetography. Our code, named GENESIS (GENEtic Stokes Inversion Strategy), employs multi-threaded parallel-processing techniques to harness the computing power of graphics processing units GPUs, along with algorithms designed to exploit the inherent parallelism of the Stokes inversion problem. Using a genetic algorithm (GA) engineered specifically for use with a GPU, we produce full-disc maps of the photospheric vector magnetic field from polarized spectral line observations recorded by the Synoptic Optical Long-term Investigations of the Sun (SOLIS) Vector Spectromagnetograph (VSM) instrument. We show the advantages of pairing a population-parallel genetic algorithm with data-parallel GPU-computing techniques, and present an overview of the Stokes inversion problem, including a description of our adaptation to the GPU-computing paradigm. Full-disc vector magnetograms derived by this method are shown, using SOLIS/VSM data observed on 2008 March 28 at 15:45 UT

Applications of GPU Computing to Control and Simulate Systems

[Abstract] This work deals with the new programming paradigm that exploits the benefits of modern Graphics Processing Units (GPUs), specifically their capacity to carry heavy calculations out for simulating systems or solving complex control strategies in real time

Doctor of Philosophy

dissertationAs the base of the software stack, system-level software is expected to provide ecient and scalable storage, communication, security and resource management functionalities. However, there are many computationally expensive functionalities at the system level, such as encryption, packet inspection, and error correction. All of these require substantial computing power. What's more, today's application workloads have entered gigabyte and terabyte scales, which demand even more computing power. To solve the rapidly increased computing power demand at the system level, this dissertation proposes using parallel graphics pro- cessing units (GPUs) in system software. GPUs excel at parallel computing, and also have a much faster development trend in parallel performance than central processing units (CPUs). However, system-level software has been originally designed to be latency-oriented. GPUs are designed for long-running computation and large-scale data processing, which are throughput-oriented. Such mismatch makes it dicult to t the system-level software with the GPUs. This dissertation presents generic principles of system-level GPU computing developed during the process of creating our two general frameworks for integrating GPU computing in storage and network packet processing. The principles are generic design techniques and abstractions to deal with common system-level GPU computing challenges. Those principles have been evaluated in concrete cases including storage and network packet processing applications that have been augmented with GPU computing. The signicant performance improvement found in the evaluation shows the eectiveness and eciency of the proposed techniques and abstractions. This dissertation also presents a literature survey of the relatively young system-level GPU computing area, to introduce the state of the art in both applications and techniques, and also their future potentials

Multi-GPU acceleration of large-scale density-based topology optimization

This work presents a parallel implementation of density-based topology optimization using distributed GPU computing systems. The use of multiple GPU devices allows us accelerating the computing process and increasing the device memory available for GPU computing. This increment of device memory enables us to address large models that commonly do not fit into one GPU device. The most modern scientific computers incorporate these devices to design energy-efficient, low-cost, and high-computing power systems. However, we should adopt the proper techniques to take advantage of the computational resources of such high-performance many-core computing systems. It is well-known that the bottleneck of density-based topology optimization is the solving of the linear elasticity problem using Finite Element Analysis (FEA) during the topology optimization iterations. We solve the linear system of equations obtained from FEA using a distributed conjugate gradient solver preconditioned by a smooth aggregation-based algebraic multigrid (AMG) using GPU computing with multiple devices. The use of aggregation-based AMG reduces memory requirements and improves the efficiency of the interpolation operation. This fact is rewarding for GPU computing. We evaluate the performance and scalability of the distributed GPU system using structured and unstructured meshes. We also test the performance using different 3D finite elements and relaxing operators. Besides, we evaluate the use of numerical approaches to increase the topology optimization performance. Finally, we present a comparison between the many-core computing instance and one efficient multi-core implementation to highlight the advantages of using GPU computing in large-scale density-based topology optimization problems.This work has been supported by the AEI/FEDER and UE under the contract DPI2016-77538-R, and by the “Fundación Séneca – Agencia de Ciencia y Tecnología de la Región de Murcia” of Spain under the contract 20911/PI/18