Optimization principles and application performance evaluation of a multithreaded GPU using CUDA
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
A performance study of general-purpose applications on graphics processors using CUDA
Journal of Parallel and Distributed Computing
An OpenCL framework for heterogeneous multicores with local memory
Proceedings of the 19th international conference on Parallel architectures and compilation techniques
Proceedings of the 19th international conference on Parallel architectures and compilation techniques
Comparing Hardware Accelerators in Scientific Applications: A Case Study
IEEE Transactions on Parallel and Distributed Systems
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Heterogeneous platforms that are consisted of CPU and add-on streaming processors are widely used in modern computer systems. These add-on processors provide substantially more computation capability and memory bandwidth than conventional multi-cores platforms. General-purpose computations can also be leveraged onto these add-on processors. In order to utilize their potential performance, programming these streaming processors is challenging because of their diverse underlying architectural characteristics. Several optimization techniques are applied on OpenCL-compatible heterogeneous platforms to achieve thread-level, data-level, and instruction-level parallelism. The architectural implications of these techniques and optimization principles are discussed. Finally, a case study of MRI-Q benchmark will be addressed to illustrate to capabilities of these optimization techniques. The experimental results reveal the speedup from non-optimized to optimized kernel can vary from 8 to 63 on different target platforms.