high performance computing on graphics processing units: hgpu.org

The use of GPU clusters for scientific applications in areas such as physics, chemistry and bioinformatics is becoming more widespread. These clusters frequently have different types of processing devices, such as CPUs and GPUs, which can themselves be heterogeneous. To use these devices in an efficient manner, it is crucial to find the right amount of work for each processor that balances the computational load among them. This problem is not only NP-hard on its essence, but also tricky due to the variety of architectures of those devices. We present PLB-HeC, a Profile-based Load-Balancing algorithm for Heterogeneous CPU-GPU Clusters that performs an online estimation of performance curve models for each GPU and CPU processor. Its main difference to existing algorithms is the generation of a non-linear system of equations representing the models and its solution using a interior point method, improving the accuracy of block distribution among processors. We implemented the algorithm in the StarPU framework and compared its performance with existing load-balancing algorithms using applications from linear algebra, stock markets and bioinformatics. We show that it reduces the application execution times in almost all scenarios, when using heterogeneous clusters with two or more machine configurations.