A Proposed Data Partitioning Approach on Heterogeneous HPC Platforms: Data Locality Perspective

We propose a new data partitioning approach to improve the performance of heterogeneous parallel applications in modern high-performance computing (HPC) systems. Existing approaches do not consider an important aspect that has a critical impact on the performance of parallel applications: the method of assigning partitions to each processor so as to minimize the communication cost and hence minimize data movement, which dominates energy and performance cost. Such an aspect for managing data locality is important for a large range of applications. Therefore, to achieve efficient data partitioning, we propose a method for distribution considering this aspect. Our algorithm seeks to minimize execution time by using two models. The first is a fine-grained computational model of heterogeneous processors, which is sufficiently adequate and accurate to guarantee efficient partitioning results that maximize utilization. The second is a communication model of heterogeneous processors to minimize data motion and hide communication overheads. The correctness of our algorithm was analyzed and validated. The complexity of our algorithm is approximately of order <inline-formula> <tex-math notation="LaTeX">$\mathbf {O}(\mathbf {p}\times \mathbf {log \,\,s}+\mathbf {p}\times \mathbf {s}^{\mathbf {2}})$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> is problem size/steps (where steps is the step size between data points in the computational model of each processor), and <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> is the number of heterogeneous processors. The experiments were performed on AZIZ supercomputer using two types of applications: an application with no dependency between its partitions, i.e., matrix multiplication, and another one with high dependency between its partitions, i.e., the Jacobi method. The results show the efficiency of our algorithm in improving performance.