CourseArchive Chapter 1 of the book introduces the concept and importance of Massively Parallel Processors in modern computing. It traces the evolution from single-core CPUs to multicore CPUs and many-thread GPUs, driven by limitations in energy consumption and heat dissipation. The chapter underscores the necessity of Parallel Programming to harness the performance potential of contemporary processors, emphasizing the distinct design philosophies of CPUs (latency-oriented) and GPUs (throughput-oriented).
The chapter highlights the motivations for massively parallel programming, pointing out that future applications will demand more speed and parallelism for increasingly complex and data-intensive tasks, such as molecular biology simulations, high-definition video processing, advanced gaming, and deep learning. It introduces the concept of Speedup in parallel computing, explains Amdahl’s Law, and discusses the challenges in achieving significant speedup due to factors like Memory Bandwidth limitations and the need for efficient data management.
Additionally, the chapter identifies key challenges in parallel programming, including the design of parallel algorithms, memory access optimization, handling diverse input data characteristics, and Synchronization overhead. It also reviews related parallel programming interfaces like OpenMP and MPI, and OpenCL, comparing their features and use cases.
The overarching goals of the book are to teach readers how to develop high-performance parallel programs, ensure correct functionality and reliability, and achieve scalability across future hardware generations. The chapter concludes with an overview of the book’s organization, outlining its four parts that cover fundamental concepts, primitive and advanced parallel patterns, and advanced practices.
1.1 Heterogeneous Parallel Computing
The evolution of Microprocessors since 2003 has seen a shift from single-core to multicore CPUs and many-thread GPUs. Multicore CPUs aim to maintain the speed of sequential programs using multiple cores, while Many-thread GPUs focus on throughput for parallel applications. CPUs are designed with a latency-oriented design, featuring complex arithmetic units and large caches. In contrast, GPUs are designed for throughput with many simple arithmetic units and high memory bandwidth. Applications with many parallel threads perform better on GPUs, whereas tasks requiring fewer threads and lower latency perform better on CPUs. Joint CPU-GPU Execution (e.g., CUDA) can optimize performance.
1.2 Why More Speed or Parallelism?
Future applications will require increased speed and parallelism for complex tasks such as molecular biology, high-definition video processing, advanced gaming, and deep learning. Enhanced user interfaces and realistic modelling are among the benefits. The adoption of Neural Network-Based Applications has been accelerated by increased computing power. Effective Data Delivery Management is crucial for parallel application performance, and CUDA provides a practical programming model for parallel implementation.
1.3 Speeding Up Real Applications
Speedup is defined as the ratio of execution time between two systems. Amdahl’s Law states that speedup is limited by the portion of the application that can be parallelized. For instance, if only 30% of an application can be parallelized, the maximum speedup is constrained. Achieving significant speedup requires extensive optimization, and Memory Bandwidth Limitations can restrict speedup. Cooperation between CPUs and GPUs is essential for optimal performance.
1.4 Challenges in Parallel Programming
Designing Parallel Algorithms with similar complexity to sequential ones is challenging. Work Efficiency and converting sequential formulations to parallel forms are important. Memory Access Optimization is crucial for memory-bound applications, and techniques for improving Data Locality are discussed. Handling erratic or uneven data distributions and adjusting thread counts is another challenge. Synchronization Overhead can be significant, requiring strategies to reduce it.
1.5 Related Parallel Programming Interfaces
OpenMP is used for shared memory multiprocessor systems, providing compiler automation and runtime support for abstraction. MPI is used for scalable cluster computing with message passing and requires joint MPI/CUDA programming for heterogeneous clusters. OpenCL offers a standardized model with language extensions and runtime APIs, similar in concepts and techniques to CUDA.
1.6 Overarching Goals
The primary goal is to develop high-performance parallel programs by understanding parallel hardware architectures. Ensuring correct functionality and reliability involves using synchronization, memory consistency, and atomicity in CUDA, as well as debugging performance bottlenecks. Scalability across future hardware generations is achieved by regularizing and localizing memory data accesses. The educational approach focuses on teaching principles and patterns through practical applications and hands-on exercises.
1.7 Organization of the Book
The book is organized into four parts:
- Part I: Fundamental Concepts (Chapters 2-6): Covers data parallelism, CUDA programming, GPU architecture, memory architecture, and performance considerations.
- Part II: Primitive Parallel Patterns (Chapters 7-12): Includes convolution, stencil, histogram, reduction, prefix sum, and merge.
- Part III: Advanced Parallel Patterns and Applications (Chapters 13-19): Discusses sorting, sparse matrix computation, graph traversal, deep learning, MRI reconstruction, and electrostatic potential map.
- Part IV: Advanced Practices (Chapters 20-22): Focuses on heterogeneous cluster programming, CUDA dynamic parallelism, and advanced features.
- Conclusion (Chapter 23): Summarizes the goals and future outlook for massively parallel programming.