Heterogeneous Computing Architectures: Challenges and Vision provides an updated vision of the state-of-the-art of heterogeneous computing systems, covering all the aspects related to their design: from the architecture and programming models to hardware/software integration and orchestration to real-time and security requirements. The transitions from multicore processors, GPU computing, and Cloud computing are not separate trends, but aspects of a single trend-mainstream; computers from desktop to smartphones are being permanently transformed into heterogeneous supercomputer clusters. The reader will get an organic perspective of modern heterogeneous systems and their future evolution.
This book constitutes the proceedings of the 5th Latin American Conference, CARLA 2018, held in Bucaramanga, Colombia, in September 2018. The 24 papers presented in this volume were carefully reviewed and selected from 38 submissions. They are organized in topical sections on: Artificial Intelligence; Accelerators; Applications; Performance Evaluation; Platforms and Infrastructures; Cloud Computing.
Modern signal processing systems require more and more processing capacity as times goes on. Previously, large increases in speed and power efficiency have come from process technology improvements. However, lately the gain from process improvements have been greatly reduced. Currently, the way forward for high-performance systems is to use specialized hardware and/or parallel designs. Application Specific Integrated Circuits (ASICs) have long been used to accelerate the processing of tasks that are too computationally heavy for more general processors. The problem with ASICs is that they are costly to develop and verify, and the product life time can be limited with newer standards. Since they are very specific the applicable domain is very narrow. More general processors are more flexible and can easily adapt to perform the functions of ASIC based designs. However, the generality comes with a performance cost that renders general designs unusable for some tasks. The question then becomes, how general can a processor be while still being power efficient and fast enough for some particular domain? Application Specific Instruction set Processors (ASIPs) are processors that target a specific application domain, and can offer enough performance with power efficiency and silicon cost that is comparable to ASICs. The flexibility allows for the same hardware design to be used over several system designs, and also for multiple functions in the same system, if some functions are not used simultaneously. One problem with ASIPs is that they are more difficult to program than a general purpose processor, given that we want efficient software. Utilizing all of the features that give an ASIP its performance advantage can be difficult at times, and new tools and methods for programming them are needed. This thesis will present ePUMA (embedded Parallel DSP platform with Unique Memory Access), an ASIP architecture that targets algorithms with predictable data access. These kinds of algorithms are very common in e.g. baseband processing or multimedia applications. The primary focus will be on the specific features of ePUMA that are utilized to achieve high performance, and how it is possible to automatically utilize them using tools. The most significant features include data permutation for conflict-free data access, and utilization of address generation features for overhead free code execution. This sometimes requires specific information; for example the exact sequences of addresses in memory that are accessed, or that some operations may be performed in parallel. This is not always available when writing code using the traditional way with traditional languages, e.g. C, as extracting this information is still a very active research topic. In the near future at least, the way that software is written needs to change to exploit all hardware features, but in many cases in a positive way. Often the problem with current methods is that code is overly specific, and that a more general abstractions are actually easier to generate code from.
Advances in Biomechanics and Tissue Regeneration covers a wide range of recent development and advances in the fields of biomechanics and tissue regeneration. It includes computational simulation, soft tissues, microfluidics, the cardiovascular system, experimental methods in biomechanics, mechanobiology and tissue regeneration. The state-of-the-art, theories and application are presented, making this book ideal for anyone who is deciding which direction to take their future research in this field. In addition, it is ideal for everyone who is exploring new fields or currently working on an interdisciplinary project in tissue biomechanics. - Combines new trends in biomechanical modelling and tissue regeneration - Offers a broad scope, covering the entire field of tissue biomechanics - Contains perspectives from engineering, medicine and biology, thus giving a holistic view of the field
The proceedings of the 5th International Workshop on Parallel Tools for High Performance Computing provide an overview on supportive software tools and environments in the fields of System Management, Parallel Debugging and Performance Analysis. In the pursuit to maintain exponential growth for the performance of high performance computers the HPC community is currently targeting Exascale Systems. The initial planning for Exascale already started when the first Petaflop system was delivered. Many challenges need to be addressed to reach the necessary performance. Scalability, energy efficiency and fault-tolerance need to be increased by orders of magnitude. The goal can only be achieved when advanced hardware is combined with a suitable software stack. In fact, the importance of software is rapidly growing. As a result, many international projects focus on the necessary software.
High-performance system architectures are increasingly exploiting heterogeneity. The HipHaC workshop aims at combining new aspects of parallel, heterogeneous, and reconfigurable microprocessor technologies with concepts of high-performance computing and, particularly, numerical solution methods. Compute- and memory-intensive applications can only benefit from the fullhardware potential if all features on all levels are taken into account in a holistic approach.
An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions. The book covers basic concepts and necessary background knowledge, including data processing frameworks, storage systems, and hardware capabilities; offers a detailed discussion of technical issues in accelerating big data computing in terms of computation, communication, memory and storage, codesign, workload characterization and benchmarking, and system deployment and management; and surveys benchmarks and workloads for evaluating big data middleware systems. It presents a detailed discussion of big data computing systems and applications with high-performance networking, computing, and storage technologies, including state-of-the-art designs for data processing and storage systems. Finally, the book considers some advanced research topics in high-performance big data computing, including designing high-performance deep learning over big data (DLoBD) stacks and HPC cloud technologies.
Programming Massively Parallel Processors: A Hands-on Approach, Second Edition, teaches students how to program massively parallel processors. It offers a detailed discussion of various techniques for constructing parallel programs. Case studies are used to demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This guide shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. This revised edition contains more parallel programming examples, commonly-used libraries such as Thrust, and explanations of the latest tools. It also provides new coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more; increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism; and two new case studies (on MRI reconstruction and molecular visualization) that explore the latest applications of CUDA and GPUs for scientific research and high-performance computing. This book should be a valuable resource for advanced students, software engineers, programmers, and hardware engineers. - New coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more - Increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism - Two new case studies (on MRI reconstruction and molecular visualization) explore the latest applications of CUDA and GPUs for scientific research and high-performance computing
Heterogeneous Computing with OpenCL, Second Edition teaches OpenCL and parallel programming for complex systems that may include a variety of device architectures: multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units (APUs) such as AMD Fusion technology. It is the first textbook that presents OpenCL programming appropriate for the classroom and is intended to support a parallel programming course. Students will come away from this text with hands-on experience and significant knowledge of the syntax and use of OpenCL to address a range of fundamental parallel algorithms. Designed to work on multiple platforms and with wide industry support, OpenCL will help you more effectively program for a heterogeneous future. Written by leaders in the parallel computing and OpenCL communities, Heterogeneous Computing with OpenCL explores memory spaces, optimization techniques, graphics interoperability, extensions, and debugging and profiling. It includes detailed examples throughout, plus additional online exercises and other supporting materials that can be downloaded at http://www.heterogeneouscompute.org/?page_id=7 This book will appeal to software engineers, programmers, hardware engineers, and students/advanced students. Explains principles and strategies to learn parallel programming with OpenCL, from understanding the four abstraction models to thoroughly testing and debugging complete applications. Covers image processing, web plugins, particle simulations, video editing, performance optimization, and more. Shows how OpenCL maps to an example target architecture and explains some of the tradeoffs associated with mapping to various architectures Addresses a range of fundamental programming techniques, with multiple examples and case studies that demonstrate OpenCL extensions for a variety of hardware platforms
High-Performance Computing using FPGA covers the area of high performance reconfigurable computing (HPRC). This book provides an overview of architectures, tools and applications for High-Performance Reconfigurable Computing (HPRC). FPGAs offer very high I/O bandwidth and fine-grained, custom and flexible parallelism and with the ever-increasing computational needs coupled with the frequency/power wall, the increasing maturity and capabilities of FPGAs, and the advent of multicore processors which has caused the acceptance of parallel computational models. The Part on architectures will introduce different FPGA-based HPC platforms: attached co-processor HPRC architectures such as the CHREC’s Novo-G and EPCC’s Maxwell systems; tightly coupled HRPC architectures, e.g. the Convey hybrid-core computer; reconfigurably networked HPRC architectures, e.g. the QPACE system, and standalone HPRC architectures such as EPFL’s CONFETTI system. The Part on Tools will focus on high-level programming approaches for HPRC, with chapters on C-to-Gate tools (such as Impulse-C, AutoESL, Handel-C, MORA-C++); Graphical tools (MATLAB-Simulink, NI LabVIEW); Domain-specific languages, languages for heterogeneous computing(for example OpenCL, Microsoft’s Kiwi and Alchemy projects). The part on Applications will present case from several application domains where HPRC has been used successfully, such as Bioinformatics and Computational Biology; Financial Computing; Stencil computations; Information retrieval; Lattice QCD; Astrophysics simulations; Weather and climate modeling.
