Advances in GPU Research and Practice

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 algorithm s 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.

Inhaltsverzeichnis:

Part 1: Programming and Tools
1. Formal analysis techniques for reliable GPU programming: current solutions and call to action
2. SnuCL: A unified OpenCL framework for heterogeneous clusters
3. Thread communication and synchronization on massively parallel GPUs
4. Software-level task scheduling on GPUs
5. Data placement on GPUs

Part 2: Algorithms and Applications
6. Biological sequence analysis on GPU
7. Graph algorithms on GPUs
8. GPU alignment of two and three sequences
9. Augmented Block Cimmino Distributed Algorithm for solving tridiagonal systems on GPU
10. GPU computing applied to linear and mixed-integer programming
11. GPU-accelerated shortest paths computations for planar graphs
12. GPU sorting algorithms
13. MPC: An effective floating-point compression algorithm for GPUs
14. Adaptive sparse matrix representation for efficient matrix-vector multiplication

Part 3: Architecture and Performance
15. A framework for accelerating bottlenecks in GPU execution with assist warps
16. Accelerating GPU accelerators through neural algorithmic transformation
17. The need for heterogeneous network-on-chip architectures with GPGPUs: A case study with photonic interconnects
18. Accurately modeling GPGPU frequency scaling with the CRISP performance model

Part 4: Power and Reliability
19. Energy and power considerations of GPUs
20. Architecting the last-level cache for GPUs using STT-MRAM nonvolatile memory
21. Power management of mobile GPUs
22. Advances in GPU reliability research
23. Addressing hardware reliability challenges in general-purpose GPUs