High Performance Computing on Complex Environments 1st Edition by Emmanuel Jeannot, Julius Zilinskas ISBN 1118712056 9781118712054

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ISBN 10: 1118712056 
ISBN 13: 9781118712054
Author: Emmanuel Jeannot, Julius Zilinskas

With recent changes in multicore and general-purpose computing on graphics processing units, the way parallel computers are used and programmed has drastically changed. It is important to provide a comprehensive study on how to use such machines written by specialists of the domain. The book provides recent research results in high-performance computing on complex environments, information on how to efficiently exploit heterogeneous and hierarchical architectures and distributed systems, detailed studies on the impact of applying heterogeneous computing practices to real problems, and applications varying from remote sensing to tomography. The content spans topics such as Numerical Analysis for Heterogeneous and Multicore Systems; Optimization of Communication for High Performance Heterogeneous and Hierarchical Platforms; Efficient Exploitation of Heterogeneous Architectures, Hybrid CPU+GPU, and Distributed Systems; Energy Awareness in High-Performance Computing; and Applications of Heterogeneous High-Performance Computing

High Performance Computing on Complex Environments 1st Table of contents:

Part I: Fundamentals of Complex HPC Environments

• Chapter 1: Principles of Parallel and Distributed Computing

  • Parallel Architectures (Shared Memory, Distributed Memory)

  • Parallel Programming Models (MPI, OpenMP, CUDA)

  • Concepts of Concurrency, Synchronization, and Communication

  • Performance Metrics in HPC (FLOPS, Bandwidth, Latency)

• Chapter 2: Understanding Heterogeneous Architectures

  • GPUs, FPGAs, Accelerators (e.g., Xeon Phi)

  • Multi-Core and Many-Core Processors

  • Memory Hierarchies in Heterogeneous Systems

  • Programming Models for Heterogeneity (OpenCL, SYCL, specialized APIs)

• Chapter 3: Dynamic and Adaptive Computing Paradigms

  • Workload Variability and Load Imbalance

  • Dynamic Resource Provisioning and Scheduling

  • Fault Tolerance and Resilience in Large-Scale Systems

  • Adaptive Algorithms and Runtime Systems

Part II: Performance Challenges and Analysis in Complex Environments

• Chapter 4: Performance Analysis and Profiling for Complex Systems

  • Tools for Performance Measurement (e.g., MPI-profilers, hardware counters)

  • Identifying Performance Bottlenecks in Heterogeneous/Distributed Systems

  • Scalability Analysis for Complex Workloads

  • Modeling and Prediction of Performance

• Chapter 5: Communication and Data Management in Distributed Systems

  • Network Topologies and Their Impact on Performance

  • Advanced MPI Techniques and One-Sided Communication

  • Data Locality and Memory Access Patterns

  • I/O Performance and Parallel File Systems (Lustre, GPFS)

• Chapter 6: Resource Management and Scheduling in Grids and Clouds

  • Job Scheduling Algorithms for Heterogeneous Resources

  • Resource Discovery and Allocation in Distributed Infrastructures

  • Virtualization and Containerization for HPC

  • Cloud Computing Models (IaaS, PaaS, SaaS) for HPC

Part III: Optimization Techniques for Complex HPC

• Chapter 7: Optimizing for GPU and Accelerator Architectures

  • Memory Optimization for Accelerators

  • Kernel Optimization and Parallelism Exposure

  • Hybrid CPU-GPU Programming Strategies

  • Porting Existing Codes to Accelerators

• Chapter 8: Dynamic Load Balancing and Task Scheduling

  • Dynamic Task Graph Management

  • Runtime Systems for Adaptive Execution

  • Work Stealing and Data Migration Techniques

  • Hybrid Dynamic/Static Scheduling Approaches

• Chapter 9: Resilience and Fault Tolerance in Large-Scale HPC

  • Checkpoint/Restart Mechanisms

  • Fault Detection and Recovery Strategies

  • Error-Correcting Codes and Redundancy

  • Designing Fault-Tolerant Algorithms

Part IV: Applications and Emerging Trends

• Chapter 10: HPC Applications on Heterogeneous and Distributed Systems

  • Scientific Simulations (e.g., Climate Modeling, Fluid Dynamics, Materials Science)

  • Data Analytics and Machine Learning on HPC

  • Image Processing and Scientific Visualization

  • Bioinformatics and Computational Chemistry

• Chapter 11: Emerging Architectures and Programming Models

  • Exascale Computing Challenges

  • Neuromorphic Computing and Quantum Computing (Introductory)

  • New Programming Models (e.g., Chapel, Julia, future standards)

• Chapter 12: Future Challenges and Research Directions

  • Power Efficiency and Green HPC

  • Usability and Software Ecosystems for Complex HPC

  • Security and Data Privacy in Distributed HPC

  • The Convergence of HPC, Cloud, and AI

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Tags: Emmanuel Jeannot, Julius Zilinskas, Performance, Computing