Parallel Rendering on Hybrid Multi-GPU Clusters
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Parallel Texture-Based Vector Field Visualization on Curved Surfaces Using GPU Cluster Computers
Eurographics Symposium on Parallel Graphics and Visualization (2006) Alan Heirich, Bruno Raffin, and Luis Paulo dos Santos (Editors) Parallel Texture-Based Vector Field Visualization on Curved Surfaces Using GPU Cluster Computers S. Bachthaler1, M. Strengert1, D. Weiskopf2, and T. Ertl1 1Institute of Visualization and Interactive Systems, University of Stuttgart, Germany 2School of Computing Science, Simon Fraser University, Canada Abstract We adopt a technique for texture-based visualization of flow fields on curved surfaces for parallel computation on a GPU cluster. The underlying LIC method relies on image-space calculations and allows the user to visualize a full 3D vector field on arbitrary and changing hypersurfaces. By using parallelization, both the visualization speed and the maximum data set size are scaled with the number of cluster nodes. A sort-first strategy with image-space decomposition is employed to distribute the workload for the LIC computation, while a sort-last approach with an object-space partitioning of the vector field is used to increase the total amount of available GPU memory. We specifically address issues for parallel GPU-based vector field visualization, such as reduced locality of memory accesses caused by particle tracing, dynamic load balancing for changing camera parameters, and the combination of image-space and object-space decomposition in a hybrid approach. Performance measurements document the behavior of our implementation on a GPU cluster with AMD Opteron CPUs, NVIDIA GeForce 6800 Ultra GPUs, and Infiniband network connection. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Viewing algorithms I.3.3 [Three-Dimensional Graphics and Realism]: Color, shading, shadowing, and texture 1. -
Evolution of the Graphical Processing Unit
University of Nevada Reno Evolution of the Graphical Processing Unit A professional paper submitted in partial fulfillment of the requirements for the degree of Master of Science with a major in Computer Science by Thomas Scott Crow Dr. Frederick C. Harris, Jr., Advisor December 2004 Dedication To my wife Windee, thank you for all of your patience, intelligence and love. i Acknowledgements I would like to thank my advisor Dr. Harris for his patience and the help he has provided me. The field of Computer Science needs more individuals like Dr. Harris. I would like to thank Dr. Mensing for unknowingly giving me an excellent model of what a Man can be and for his confidence in my work. I am very grateful to Dr. Egbert and Dr. Mensing for agreeing to be committee members and for their valuable time. Thank you jeffs. ii Abstract In this paper we discuss some major contributions to the field of computer graphics that have led to the implementation of the modern graphical processing unit. We also compare the performance of matrix‐matrix multiplication on the GPU to the same computation on the CPU. Although the CPU performs better in this comparison, modern GPUs have a lot of potential since their rate of growth far exceeds that of the CPU. The history of the rate of growth of the GPU shows that the transistor count doubles every 6 months where that of the CPU is only every 18 months. There is currently much research going on regarding general purpose computing on GPUs and although there has been moderate success, there are several issues that keep the commodity GPU from expanding out from pure graphics computing with limited cache bandwidth being one. -
Literature Review and Implementation Overview: High Performance
LITERATURE REVIEW AND IMPLEMENTATION OVERVIEW: HIGH PERFORMANCE COMPUTING WITH GRAPHICS PROCESSING UNITS FOR CLASSROOM AND RESEARCH USE APREPRINT Nathan George Department of Data Sciences Regis University Denver, CO 80221 [email protected] May 18, 2020 ABSTRACT In this report, I discuss the history and current state of GPU HPC systems. Although high-power GPUs have only existed a short time, they have found rapid adoption in deep learning applications. I also discuss an implementation of a commodity-hardware NVIDIA GPU HPC cluster for deep learning research and academic teaching use. Keywords GPU · Neural Networks · Deep Learning · HPC 1 Introduction, Background, and GPU HPC History High performance computing (HPC) is typically characterized by large amounts of memory and processing power. HPC, sometimes also called supercomputing, has been around since the 1960s with the introduction of the CDC STAR-100, and continues to push the limits of computing power and capabilities for large-scale problems [1, 2]. However, use of graphics processing unit (GPU) in HPC supercomputers has only started in the mid to late 2000s [3, 4]. Although graphics processing chips have been around since the 1970s, GPUs were not widely used for computations until the 2000s. During the early 2000s, GPU clusters began to appear for HPC applications. Most of these clusters were designed to run large calculations requiring vast computing power, and many clusters are still designed for that purpose [5]. GPUs have been increasingly used for computations due to their commodification, following Moore’s Law (demonstrated arXiv:2005.07598v1 [cs.DC] 13 May 2020 in Figure 1), and usage in specific applications like neural networks. -
GPU Accelerated Approach to Numerical Linear Algebra and Matrix Analysis with CFD Applications
University of Central Florida STARS HIM 1990-2015 2014 GPU Accelerated Approach to Numerical Linear Algebra and Matrix Analysis with CFD Applications Adam Phillips University of Central Florida Part of the Mathematics Commons Find similar works at: https://stars.library.ucf.edu/honorstheses1990-2015 University of Central Florida Libraries http://library.ucf.edu This Open Access is brought to you for free and open access by STARS. It has been accepted for inclusion in HIM 1990-2015 by an authorized administrator of STARS. For more information, please contact [email protected]. Recommended Citation Phillips, Adam, "GPU Accelerated Approach to Numerical Linear Algebra and Matrix Analysis with CFD Applications" (2014). HIM 1990-2015. 1613. https://stars.library.ucf.edu/honorstheses1990-2015/1613 GPU ACCELERATED APPROACH TO NUMERICAL LINEAR ALGEBRA AND MATRIX ANALYSIS WITH CFD APPLICATIONS by ADAM D. PHILLIPS A thesis submitted in partial fulfilment of the requirements for the Honors in the Major Program in Mathematics in the College of Sciences and in The Burnett Honors College at the University of Central Florida Orlando, Florida Spring Term 2014 Thesis Chair: Dr. Bhimsen Shivamoggi c 2014 Adam D. Phillips All Rights Reserved ii ABSTRACT A GPU accelerated approach to numerical linear algebra and matrix analysis with CFD applica- tions is presented. The works objectives are to (1) develop stable and efficient algorithms utilizing multiple NVIDIA GPUs with CUDA to accelerate common matrix computations, (2) optimize these algorithms through CPU/GPU memory allocation, GPU kernel development, CPU/GPU communication, data transfer and bandwidth control to (3) develop parallel CFD applications for Navier Stokes and Lattice Boltzmann analysis methods. -
Introduction to Gpus for HPC
CSC 391/691: GPU Programming Fall 2011 Introduction to GPUs for HPC Copyright © 2011 Samuel S. Cho High Performance Computing • Speed: Many problems that are interesting to scientists and engineers would take a long time to execute on a PC or laptop: months, years, “never”. • Size: Many problems that are interesting to scientists and engineers can’t fit on a PC or laptop with a few GB of RAM or a few 100s of GB of disk space. • Supercomputers or clusters of computers can make these problems practically numerically solvable. Scientific and Engineering Problems • Simulations of physical phenomena such as: • Weather forecasting • Earthquake forecasting • Galaxy formation • Oil reservoir management • Molecular dynamics • Data Mining: Finding needles of critical information in a haystack of data such as: • Bioinformatics • Signal processing • Detecting storms that might turn into hurricanes • Visualization: turning a vast sea of data into pictures that scientists can understand. • At its most basic level, all of these problems involve many, many floating point operations. Hardware Accelerators • In HPC, an accelerator is hardware component whose role is to speed up some aspect of the computing workload. • In the olden days (1980s), Not an accelerator* supercomputers sometimes had array processors, which did vector operations on arrays • PCs sometimes had floating point accelerators: little chips that did the floating point calculations in hardware rather than software. Not an accelerator* *Okay, I lied. To Accelerate Or Not To Accelerate • Pro: • They make your code run faster. • Cons: • They’re expensive. • They’re hard to program. • Your code may not be cross-platform. Why GPU for HPC? • Graphics Processing Units (GPUs) were originally designed to accelerate graphics tasks like image rendering. -
NVIDIA Index Accelerated Computing for Visualizing Cholla's Galactic
NVIDIA IndeX Accelerated Computing for Visualizing Cholla’s Galactic Winds Evan Schneider Brant Robertson Alexander Kuhn Christopher Lux Marc Nienhaus Princeton University University of California NVIDIA NVIDIA NVIDIA [email protected] Santa Cruz [email protected] [email protected] [email protected] [email protected] Abstract Galactic winds – outflows of gas driven out of galaxies by the combined effects of thousands of supernovae – are a crucial feature of galaxy evolution. By removing gas from galaxies, they regulate future star formation, and distribute the dust and heavy elements formed in stars throughout the Universe. Despite their importance, a complete theoretical picture of these winds has been elusive. Simulating the complicated interaction between the hot, high pressure gas created by supernovae and the cooler, high density gas in the galaxy disk requires massive computational resources and highly sophisticated software. In addition, galactic wind simulations generate terabytes of output posing additional challenges regarding the effective analysis of the simulated physical processes. In order to address those challenges, we present NVIDIA IndeX as a scalable framework to visualize the simulation output. The framework features a streaming- based architecture to interactively explore simulation results in distributed multi-GPU environments. We demonstrate how to customize specialized sampling programs for volume and surface rendering to cover specific analysis questions of galactic wind simulations. This provides an extensive level of control over the visualization while efficiently using available resources to achieve high levels of performance and visual accuracy. Galactic Winds. When a galaxy experiences a period of rapid star formation, the combined effect of thousands of supernovae exploding within the disk drives gas to flow out of the galaxy and into its surroundings, as seen in the simulation rendered here. -
Locality-Centric Data and Threadblock Management for Massive Gpus
2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO) Locality-Centric Data and Threadblock Management for Massive GPUs Mahmoud Khairy Vadim Nikiforov David Nellans Timothy G. Rogers Purdue University Purdue University NVIDIA Purdue University [email protected] [email protected] [email protected] [email protected] Abstract—Recent work has shown that building GPUs with Single Logical GPU Controller chip hundreds of SMs in a single monolithic chip will not be practical (Kernel/TB Scheduler, ..) due to slowing growth in transistor density, low chip yields, and GPU .….. GPU GPU GPU ..... HBM photoreticle limitations. To maintain performance scalability, HBM Chiplet Chiplet proposals exist to aggregate discrete GPUs into a larger virtual GPU and decompose a single GPU into multiple-chip-modules Switch with increased aggregate die area. These approaches introduce Inter-Chiplet Connection Network non-uniform memory access (NUMA) effects and lead to .….. GPU GPU decreased performance and energy-efficiency if not managed HBM GPU ..... HBM GPU Chiplet appropriately. To overcome these effects, we propose a holistic Chiplet Locality-Aware Data Management (LADM) system designed to Silicon Interposer or Package Substrate operate on massive logical GPUs composed of multiple discrete Fig. 1: Future massive logical GPU containing multiple dis- devices, which are themselves composed of chiplets. LADM has three key components: a threadblock-centric index analysis, a crete GPUs, which are themselves composed of chiplets in a runtime system that performs data placement and threadblock hierarchical interconnect. scheduling, and an adaptive cache insertion policy. The runtime combines information from the static analysis with topology 1 information to proactively optimize data placement, threadblock systems, chiplet based architectures are desirable because scheduling, and remote data caching, minimizing off-chip traffic. -
PC Hardware Contents
PC Hardware Contents 1 Computer hardware 1 1.1 Von Neumann architecture ...................................... 1 1.2 Sales .................................................. 1 1.3 Different systems ........................................... 2 1.3.1 Personal computer ...................................... 2 1.3.2 Mainframe computer ..................................... 3 1.3.3 Departmental computing ................................... 4 1.3.4 Supercomputer ........................................ 4 1.4 See also ................................................ 4 1.5 References ............................................... 4 1.6 External links ............................................. 4 2 Central processing unit 5 2.1 History ................................................. 5 2.1.1 Transistor and integrated circuit CPUs ............................ 6 2.1.2 Microprocessors ....................................... 7 2.2 Operation ............................................... 8 2.2.1 Fetch ............................................. 8 2.2.2 Decode ............................................ 8 2.2.3 Execute ............................................ 9 2.3 Design and implementation ...................................... 9 2.3.1 Control unit .......................................... 9 2.3.2 Arithmetic logic unit ..................................... 9 2.3.3 Integer range ......................................... 10 2.3.4 Clock rate ........................................... 10 2.3.5 Parallelism ......................................... -
Overcoming Challenges in Predictive Modeling of Laser-Plasma Interaction Scenarios
Chapter 5 Overcoming Challenges in Predictive Modeling of Laser-Plasma Interaction Scenarios. The Sinuous Route from Advanced Machine Learning to Deep Learning Andreea Mihailescu Additional information is available at the end of the chapter http://dx.doi.org/10.5772/intechopen.72844 Abstract The interaction of ultrashort and intense laser pulses with solid targets and dense plasmas is a rapidly developing area of physics, this being mostly due to the significant advance- ments in laser technology. There is, thus, a growing interest in diagnosing as accurately as possible the numerous phenomena related to the absorption and reflection of laser radia- tion. At the same time, envisaged experiments are in high demand of increased accuracy simulation software. As laser-plasma interaction modelings are experiencing a transition from computationally-intensive to data-intensive problems, traditional codes employed so far are starting to show their limitations. It is in this context that predictive modelings of laser-plasma interaction experiments are bound to reshape the definition of simulation software. This chapter focuses an entire class of predictive systems incorporating big data, advanced machine learning algorithms and deep learning, with improved accuracy and speed. Making use of terabytes of already available information (literature as well as simulation and experimental data) these systems enable the discovery and understanding of various physical phenomena occurring during interaction, hence allowing researchers to set up controlled experiments at optimal parameters. A comparative discussion in terms of challenges, advantages, bottlenecks, performances and suitability of laser-plasma inter- action predictive systems is ultimately provided. Keywords: predictive modeling, machine learning, deep learning, big data, cloud computing, laser-plasma interaction modeling 1. -
NVIDIA A100 Tensor Core GPU Architecture UNPRECEDENTED ACCELERATION at EVERY SCALE
NVIDIA A100 Tensor Core GPU Architecture UNPRECEDENTED ACCELERATION AT EVERY SCALE V1.0 Table of Contents Introduction 7 Introducing NVIDIA A100 Tensor Core GPU - our 8th Generation Data Center GPU for the Age of Elastic Computing 9 NVIDIA A100 Tensor Core GPU Overview 11 Next-generation Data Center and Cloud GPU 11 Industry-leading Performance for AI, HPC, and Data Analytics 12 A100 GPU Key Features Summary 14 A100 GPU Streaming Multiprocessor (SM) 15 40 GB HBM2 and 40 MB L2 Cache 16 Multi-Instance GPU (MIG) 16 Third-Generation NVLink 16 Support for NVIDIA Magnum IO™ and Mellanox Interconnect Solutions 17 PCIe Gen 4 with SR-IOV 17 Improved Error and Fault Detection, Isolation, and Containment 17 Asynchronous Copy 17 Asynchronous Barrier 17 Task Graph Acceleration 18 NVIDIA A100 Tensor Core GPU Architecture In-Depth 19 A100 SM Architecture 20 Third-Generation NVIDIA Tensor Core 23 A100 Tensor Cores Boost Throughput 24 A100 Tensor Cores Support All DL Data Types 26 A100 Tensor Cores Accelerate HPC 28 Mixed Precision Tensor Cores for HPC 28 A100 Introduces Fine-Grained Structured Sparsity 31 Sparse Matrix Definition 31 Sparse Matrix Multiply-Accumulate (MMA) Operations 32 Combined L1 Data Cache and Shared Memory 33 Simultaneous Execution of FP32 and INT32 Operations 34 A100 HBM2 and L2 Cache Memory Architectures 34 ii NVIDIA A100 Tensor Core GPU Architecture A100 HBM2 DRAM Subsystem 34 ECC Memory Resiliency 35 A100 L2 Cache 35 Maximizing Tensor Core Performance and Efficiency for Deep Learning Applications 37 Strong Scaling Deep Learning -
Calcolo Ad Alte Prestazioni Su Architettura Ibrida Cpu - Gpu
ALMA MATER STUDIORUM - UNIVERSITA’ DI BOLOGNA SEDE DI CESENA FACOLTA’ DI SCIENZE MATEMATICHE, FISICHE E NATURALI CORSO DI LAUREA IN SCIENZE DELL’INFORMAZIONE CALCOLO AD ALTE PRESTAZIONI SU ARCHITETTURA IBRIDA CPU - GPU Tesi di Laurea in Biofisica delle Reti Neurali e loro applicazioni Relatore Presentata da Chiar.mo Prof. Renato Campanini Luca Benini Co-Relatore Dott. Matteo Roffilli Sessione II Anno Accademico 2005/2006 The best way to predict the future is to invent it. Alan Kay Ringraziamenti Nulla di tutto ci`oche `epresente in questo volume sarebbe stato possibile senza la disponibilit`adel prof. Campanini e del dott. Roffilli che con la loro esperienza e disponibilit`ami hanno permesso di realizzare tutto ci`o. Vorrei ringraziare tutti i ragazzi dell’ex RadioLab, perch`esolo dal caos pu`onascere una stella danzante. III Indice 1 Calcolabilit`ae HPC 1 1.1 Calcolabilit`a. 1 1.1.1 Entscheindungsproblem e la Macchina di Turing . 1 1.2 Tecnologie per il calcolo ad alte prestazioni . 3 1.2.1 Supercomputer . 4 1.2.2 Processori Vettoriali . 5 1.2.3 SIMD . 7 1.2.4 Grid computing . 10 2 Graphics Processing Unit 15 2.1 Evoluzione delle unit`agrafiche programmabili . 15 2.2 Passi per la realizzazione di una vista 3D . 16 2.2.1 Primitive . 16 2.2.2 Frammenti . 17 2.2.3 Shader . 18 2.3 Architettura di una GPU . 20 2.3.1 NVIDIA GeForce 7800 GTX . 20 3 C for graphics 25 3.1 Caratteristiche del linguaggio . 25 3.1.1 Vantaggi dello shading di alto livello . -
An Application Developed for Simulation of Electrical Excitation and Conduction in a 3D Human Heart Di Yu University of South Florida, [email protected]
University of South Florida Scholar Commons Graduate Theses and Dissertations Graduate School January 2013 An Application Developed for Simulation of Electrical Excitation and Conduction in a 3D Human Heart Di Yu University of South Florida, [email protected] Follow this and additional works at: http://scholarcommons.usf.edu/etd Part of the Computer Sciences Commons Scholar Commons Citation Yu, Di, "An Application Developed for Simulation of Electrical Excitation and Conduction in a 3D Human Heart" (2013). Graduate Theses and Dissertations. http://scholarcommons.usf.edu/etd/4620 This Thesis is brought to you for free and open access by the Graduate School at Scholar Commons. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. An Application Developed for Simulation of Electrical Excitation and Conduction in a 3D Human Heart by Di Yu A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science Department of Computer Science and Engineering College of Engineering University of South Florida Major Professor: Yicheng Tu, Ph.D. Hui Yang, Ph.D. Swaroop Ghosh, Ph.D. Date of Approval: March 21, 2013 Keywords: GPGPU, Parallel Algorithm, CUDA, SIMD, Cellular Automata Copyright © 2013, Di Yu 0 TABLE OF CONTENTS LIST OF FIGURES ............................................................................................... iii ABSTRACT .......................................................................................................