包邮大规模并行处理器程序设计

preface
acknowledgments
dedication
chapter 1 introduction
　1.1　gpus as parallel computers
　1.2　architecture of a modern gpu
　1.3　why more speed or parallelism?
　1.4　parallel programming languages and models
　1.5　overarching goals
　1.6　organization of the book
chapter 2　history of gpu computing
　2.1　evolution of graphics pipelines
　　2.1.1　the era of fixed-function graphics pipelines
　　2.1.2　evolution of programmable real-time graphics
　　2.1.3　unified graphics and computing processors
　　2.1.4　gpgpu: an intermediate step
　2.2　gpu computing
　　2.2.1　scalable gpus
　　2.2.2　recent developments
　2.3　future trends
chapter 3　introduction to cuda
　3.1　data parallelism
　3.2　cuda program structure
　3.3　a matrix-matrix multiplication example
　3.4　device memories and data transfer
　3.5　kernel functions and threading
　3.6　summary
　　3.6.1　function declarations
　　3.6.2　kernel launch
　　3.6.3　predefined variables
　　3.6.4　runtime apl
chapter 4　cuda threads
　4.1　cuda thread organization
　4.2　using b]ockldx and threadidx
　4.3　synchronization and transparent scalability
　4.4　thread assignment
　4.5　thread scheduling and latency tolerance
　4.6　summary
　4.7　exercises
chapter 5　cudatm memories
　5.1　importance of memory access efficiency
　5.2　cuda device memory types
　5.3　a strategy for reducing global memory traffic
　5.4　memory as a limiting factor to parallelism
　5.5　summary
　5.6　exercises
chapter 6　performance considerations
　6.1　more on thread execution
　6.2　global memory bandwidth
　6.3　dynamic partitioning of sm resources
　6.4　data prefetching
　6.5　instruction mix
　6.6　thread granularity
　6.7　measured performance and summary
　6.8　exercises
chapter 7　floating point considerations
　7.1　floating-point format
　7.1.1　normalized representation of m
　7.1.2　excess encoding of e
　7.2　representable numbers
　7.3　special bit patterns and precision
　7.4　arithmetic accuracy and rounding
　7.5　algorithm considerations
　7.6　summary
　7.7　exercises
chapter 8　application case study: advanced mri reconstruction
　8.1　application background
　8.2　iterative reconstruction
　8.3　computing fhd
　　step 1. determine the kernel parallelism structure
　　step 2. getting around the memory bandwidth limitation.
　　step 3. using hardware trigonometry functions
　　step 4. experimental performance tuning
　8.4　final evaluation
　8.5　exercises
chapter 9　application case study: molecular visualization and analysis
chapter 10　parallel programming and computational thinking
chapter 11　a brief introduction to opencltm
chapter 12　conclusion and'future outlook
appendix a　matrix multiplication host-only version source code
appendix b　gpu compute capabilities
index