LibWater: heterogeneous distributed computing made easy
Proceedings of the 27th international ACM conference on International conference on supercomputing
Implementing OmpSs support for regions of data in architectures with multiple address spaces
Proceedings of the 27th international ACM conference on International conference on supercomputing
Semi-automatic restructuring of offloadable tasks for many-core accelerators
SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Fluidic Kernels: Cooperative Execution of OpenCL Programs on Multiple Heterogeneous Devices
Proceedings of Annual IEEE/ACM International Symposium on Code Generation and Optimization
ACM SIGAda Ada Letters
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Clusters of GPUs are emerging as a new computational scenario. Programming them requires the use of hybrid models that increase the complexity of the applications, reducing the productivity of programmers. We present the implementation of OmpSs for clusters of GPUs, which supports asynchrony and heterogeneity for task parallelism. It is based on annotating a serial application with directives that are translated by the compiler. With it, the same program that runs sequentially in a node with a single GPU can run in parallel in multiple GPUs either local (single node) or remote (cluster of GPUs). Besides performing a task-based parallelization, the runtime system moves the data as needed between the different nodes and GPUs minimizing the impact of communication by using affinity scheduling, caching, and by overlapping communication with the computational task. We show several applications programmed with OmpSs and their performance with multiple GPUs in a local node and in remote nodes. The results show good tradeoff between performance and effort from the programmer.