Memory Warp

In the 1990s, a faddish pseudoscience, repressed memory theory, destroyed millions of American families by creating false memories of childhood sexual abuse. At the time, Mark Pendergrast published his widely acclaimed book Victims of Memory, exposing the false nature of the science and counseling techniques that were alienating teenagers and grown children from their families. In Memory Warp, Pendergrast revisits that subject, updating his research and describing where it stands now, in 2017. All notable scientific researchers in the field of memory now agree that repressed-memory theory is misguided and harmful--that the "memories" produced are false, and that those accused--mostly parents and other family members and caregivers--have suffered greatly from false allegations of horrible crimes against their own children. But does that mean that the scourge is now behind us? Unfortunately, no. The disproven theories of repressed memory continue to resurface throughout Amercan cultural life and, although usually more subtly, in the work of therapists. In this new book, Pendergrast provides a lively social history of our recent past, documenting how this incredible juggernaut of pseudoscience, which caused so much harm, came to be. But more importantly, the book also shows how these misguided theories continue to fester. And how, if we fail to learn from the lurid history of this movement, we may face another outbreak. Many authoritative books have have appeared on this subject over the years, including those of memory researchers who have confirmed Pendergrast's conclusions. But Memory Warp is the first to describe the threat that continues to exist today.

Programming Massively Parallel Processors: A Hands-on Approach shows both students and professionals alike the basic concepts of parallel programming and GPU architecture. Concise, intuitive, and practical, it is based on years of road-testing in the authors' own parallel computing courses. Various techniques for constructing and optimizing parallel programs are explored in detail, while case studies demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. The new edition includes updated coverage of CUDA, including the newer libraries such as CuDNN. New chapters on frequently used parallel patterns have been added, and case studies have been updated to reflect current industry practices. Parallel Patterns Introduces new chapters on frequently used parallel patterns (stencil, reduction, sorting) and major improvements to previous chapters (convolution, histogram, sparse matrices, graph traversal, deep learning) Ampere Includes a new chapter focused on GPU architecture and draws examples from recent architecture generations, including Ampere Systematic Approach Incorporates major improvements to abstract discussions of problem decomposition strategies and performance considerations, with a new optimization checklist

Beyond simulation and algorithm development, many developers increasingly use MATLAB even for product deployment in computationally heavy fields. This often demands that MATLAB codes run faster by leveraging the distributed parallelism of Graphics Processing Units (GPUs). While MATLAB successfully provides high-level functions as a simulation tool for rapid prototyping, the underlying details and knowledge needed for utilizing GPUs make MATLAB users hesitate to step into it. Accelerating MATLAB with GPUs offers a primer on bridging this gap. Starting with the basics, setting up MATLAB for CUDA (in Windows, Linux and Mac OS X) and profiling, it then guides users through advanced topics such as CUDA libraries. The authors share their experience developing algorithms using MATLAB, C++ and GPUs for huge datasets, modifying MATLAB codes to better utilize the computational power of GPUs, and integrating them into commercial software products. Throughout the book, they demonstrate many example codes that can be used as templates of C-MEX and CUDA codes for readers’ projects. Download example codes from the publisher's website: http://booksite.elsevier.com/9780124080805/ Shows how to accelerate MATLAB codes through the GPU for parallel processing, with minimal hardware knowledge Explains the related background on hardware, architecture and programming for ease of use Provides simple worked examples of MATLAB and CUDA C codes as well as templates that can be reused in real-world projects

The book then details the thought behind CUDA and teaches how to create, analyze, and debug CUDA applications. Throughout, the focus is on software engineering issues: how to use CUDA in the context of existing application code, with existing compilers, languages, software tools, and industry-standard API libraries."--Pub. desc.

Advances in GPU Research and Practice focuses on research and practices in GPU based systems. The topics treated cover a range of issues, ranging from hardware and architectural issues, to high level issues, such as application systems, parallel programming, middleware, and power and energy issues. Divided into six parts, this edited volume provides the latest research on GPU computing. Part I: Architectural Solutions focuses on the architectural topics that improve on performance of GPUs, Part II: System Software discusses OS, compilers, libraries, programming environment, languages, and paradigms that are proposed and analyzed to help and support GPU programmers. Part III: Power and Reliability Issues covers different aspects of energy, power, and reliability concerns in GPUs. Part IV: Performance Analysis illustrates mathematical and analytical techniques to predict different performance metrics in GPUs. Part V: Algorithms presents how to design efficient algorithms and analyze their complexity for GPUs. Part VI: Applications and Related Topics provides use cases and examples of how GPUs are used across many sectors. Discusses how to maximize power and obtain peak reliability when designing, building, and using GPUs Covers system software (OS, compilers), programming environments, languages, and paradigms proposed to help and support GPU programmers Explains how to use mathematical and analytical techniques to predict different performance metrics in GPUs Illustrates the design of efficient GPU algorithms in areas such as bioinformatics, complex systems, social networks, and cryptography Provides applications and use case scenarios in several different verticals, including medicine, social sciences, image processing, and telecommunications

Break into the powerful world of parallel GPU programming with this down-to-earth, practical guide Designed for professionals across multiple industrial sectors, Professional CUDA C Programming presents CUDA -- a parallel computing platform and programming model designed to ease the development of GPU programming -- fundamentals in an easy-to-follow format, and teaches readers how to think in parallel and implement parallel algorithms on GPUs. Each chapter covers a specific topic, and includes workable examples that demonstrate the development process, allowing readers to explore both the "hard" and "soft" aspects of GPU programming. Computing architectures are experiencing a fundamental shift toward scalable parallel computing motivated by application requirements in industry and science. This book demonstrates the challenges of efficiently utilizing compute resources at peak performance, presents modern techniques for tackling these challenges, while increasing accessibility for professionals who are not necessarily parallel programming experts. The CUDA programming model and tools empower developers to write high-performance applications on a scalable, parallel computing platform: the GPU. However, CUDA itself can be difficult to learn without extensive programming experience. Recognized CUDA authorities John Cheng, Max Grossman, and Ty McKercher guide readers through essential GPU programming skills and best practices in Professional CUDA C Programming, including: CUDA Programming Model GPU Execution Model GPU Memory model Streams, Event and Concurrency Multi-GPU Programming CUDA Domain-Specific Libraries Profiling and Performance Tuning The book makes complex CUDA concepts easy to understand for anyone with knowledge of basic software development with exercises designed to be both readable and high-performance. For the professional seeking entrance to parallel computing and the high-performance computing community, Professional CUDA C Programming is an invaluable resource, with the most current information available on the market.

This two-volume set, LNCS 11317 and 12318, constitutes the thoroughly refereed proceedings of the 4th International Joint Conference, APWeb-WAIM 2020, held in Tianjin, China, in September 2020. Due to the COVID-19 pandemic the conference was organizedas a fully online conference. The 42 full papers presented together with 17 short papers, and 6 demonstration papers were carefully reviewed and selected from 180 submissions. The papers are organized around the following topics: Big Data Analytics; Graph Data and Social Networks; Knowledge Graph; Recommender Systems; Information Extraction and Retrieval; Machine Learning; Blockchain; Data Mining; Text Analysis and Mining; Spatial, Temporal and Multimedia Databases; Database Systems; and Demo.

The two volumes LNCS 8805 and 8806 constitute the thoroughly refereed post-conference proceedings of 18 workshops held at the 20th International Conference on Parallel Computing, Euro-Par 2014, in Porto, Portugal, in August 2014. The 100 revised full papers presented were carefully reviewed and selected from 173 submissions. The volumes include papers from the following workshops: APCI&E (First Workshop on Applications of Parallel Computation in Industry and Engineering - BigDataCloud (Third Workshop on Big Data Management in Clouds) - DIHC (Second Workshop on Dependability and Interoperability in Heterogeneous Clouds) - FedICI (Second Workshop on Federative and Interoperable Cloud Infrastructures) - Hetero Par (12th International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms) - HiBB (5th Workshop on High Performance Bioinformatics and Biomedicine) - LSDVE (Second Workshop on Large Scale Distributed Virtual Environments on Clouds and P2P) - MuCoCoS (7th International Workshop on Multi-/Many-core Computing Systems) - OMHI (Third Workshop on On-chip Memory Hierarchies and Interconnects) - PADAPS (Second Workshop on Parallel and Distributed Agent-Based Simulations) - PROPER (7th Workshop on Productivity and Performance) - Resilience (7th Workshop on Resiliency in High Performance Computing with Clusters, Clouds, and Grids) - REPPAR (First International Workshop on Reproducibility in Parallel Computing) - ROME (Second Workshop on Runtime and Operating Systems for the Many Core Era) - SPPEXA (Workshop on Software for Exascale Computing) - TASUS (First Workshop on Techniques and Applications for Sustainable Ultrascale Computing Systems) - UCHPC (7th Workshop on Un Conventional High Performance Computing) and VHPC (9th Workshop on Virtualization in High-Performance Cloud Computing.