In view of the growing presence and popularity of multicore and manycore processors, accelerators, and coprocessors, as well as clusters using such computing devices, the development of efficient parallel applications has become a key challenge to be able to exploit the performance of such systems. This book covers the scope of parallel programming for modern high performance computing systems. It first discusses selected and popular state-of-the-art computing devices and systems available today, These include multicore CPUs, manycore (co)processors, such as Intel Xeon Phi, accelerators, such as GPUs, and clusters, as well as programming models supported on these platforms. It next introduces parallelization through important programming paradigms, such as master-slave, geometric Single Program Multiple Data (SPMD) and divide-and-conquer. The practical and useful elements of the most popular and important APIs for programming parallel HPC systems are discussed, including MPI, OpenMP, Pthreads, CUDA, OpenCL, and OpenACC. It also demonstrates, through selected code listings, how selected APIs can be used to implement important programming paradigms. Furthermore, it shows how the codes can be compiled and executed in a Linux environment. The book also presents hybrid codes that integrate selected APIs for potentially multi-level parallelization and utilization of heterogeneous resources, and it shows how to use modern elements of these APIs. Selected optimization techniques are also included, such as overlapping communication and computations implemented using various APIs. Features: Discusses the popular and currently available computing devices and cluster systems Includes typical paradigms used in parallel programs Explores popular APIs for programming parallel applications Provides code templates that can be used for implementation of paradigms Provides hybrid code examples allowing multi-level parallelization Covers the optimization of parallel programs

Describing state-of-the-art solutions in distributed system architectures, Integration of Services into Workflow Applications presents a concise approach to the integration of loosely coupled services into workflow applications. It discusses key challenges related to the integration of distributed systems and proposes solutions, both in terms of theoretical aspects such as models and workflow scheduling algorithms, and technical solutions such as software tools and APIs. The book provides an in-depth look at workflow scheduling and proposes a way to integrate several different types of services into one single workflow application. It shows how these components can be expressed as services that can subsequently be integrated into workflow applications. The workflow applications are often described as acyclic graphs with dependencies which allow readers to define complex scenarios in terms of basic tasks. Presents state-of-the-art solutions to challenges in multi-domain workflow application definition, optimization, and execution Proposes a uniform concept of a service that can represent executable components in all major distributed software architectures used today Discusses an extended model with determination of data flows among parallel paths of a workflow application Since workflow applications often process big data, the book explores the dynamic management of data with various storage constraints during workflow execution. It addresses several practical problems related to data handling, including data partitioning for parallel processing next to service selection and scheduling, processing data in batches or streams, and constraints on data sizes that can be processed at the same time by service instances. Illustrating several workflow applications that were proposed, implemented, and benchmarked in a real BeesyCluster environment, the book includes templates for

In view of the growing presence and popularity of multicore and manycore processors, accelerators, and coprocessors, as well as clusters using such computing devices, the development of efficient parallel applications has become a key challenge to be able to exploit the performance of such systems. This book covers the scope of parallel programming for modern high performance computing systems. It first discusses selected and popular state-of-the-art computing devices and systems available today, These include multicore CPUs, manycore (co)processors, such as Intel Xeon Phi, accelerators, such as GPUs, and clusters, as well as programming models supported on these platforms. It next introduces parallelization through important programming paradigms, such as master-slave, geometric Single Program Multiple Data (SPMD) and divide-and-conquer. The practical and useful elements of the most popular and important APIs for programming parallel HPC systems are discussed, including MPI, OpenMP, Pthreads, CUDA, OpenCL, and OpenACC. It also demonstrates, through selected code listings, how selected APIs can be used to implement important programming paradigms. Furthermore, it shows how the codes can be compiled and executed in a Linux environment. The book also presents hybrid codes that integrate selected APIs for potentially multi-level parallelization and utilization of heterogeneous resources, and it shows how to use modern elements of these APIs. Selected optimization techniques are also included, such as overlapping communication and computations implemented using various APIs. Features: Discusses the popular and currently available computing devices and cluster systems Includes typical paradigms used in parallel programs Explores popular APIs for programming parallel applications Provides code templates that can be used for implementation of paradigms Provides hybrid code examples allowing multi-level parallelization Covers the optimization of parallel programs

Describing state-of-the-art solutions in distributed system architectures, Integration of Services into Workflow Applications presents a concise approach to the integration of loosely coupled services into workflow applications. It discusses key challenges related to the integration of distributed systems and proposes solutions, both in terms of theoretical aspects such as models and workflow scheduling algorithms, and technical solutions such as software tools and APIs. The book provides an in-depth look at workflow scheduling and proposes a way to integrate several different types of services into one single workflow application. It shows how these components can be expressed as services that can subsequently be integrated into workflow applications. The workflow applications are often described as acyclic graphs with dependencies which allow readers to define complex scenarios in terms of basic tasks. Presents state-of-the-art solutions to challenges in multi-domain workflow application definition, optimization, and execution Proposes a uniform concept of a service that can represent executable components in all major distributed software architectures used today Discusses an extended model with determination of data flows among parallel paths of a workflow application Since workflow applications often process big data, the book explores the dynamic management of data with various storage constraints during workflow execution. It addresses several practical problems related to data handling, including data partitioning for parallel processing next to service selection and scheduling, processing data in batches or streams, and constraints on data sizes that can be processed at the same time by service instances. Illustrating several workflow applications that were proposed, implemented, and benchmarked in a real BeesyCluster environment, the book includes templates for