High-bandwidth On-Package Memory (OPM) innovates the conventional memory hierarchy by augmenting a new on-package layer between classic on-chip cache and off-chip DRAM. Due to its relative location and capacity, OPM is often used as a new type of LLC. Despite the adaptation in modern processors, the performance and power impact of OPM on HPC applications, especially scientific kernels, is still unknown. In this paper, we fill this gap by conducting a comprehensive evaluation for a wide spectrum of scientific kernels with a large amount of representative inputs, including dense, sparse and medium, on two Intel OPMs: eDRAM on multicore Broadwell and MCDRAM on manycore Knights Landing. Guided by our general optimization models, we demonstrate OPM's effectiveness for easing programmers' tuning efforts to reach ideal throughput for both compute-bound and memory-bound applications.

We address the problem of fusing array operations based on criteria such as shape compatibility, data reuse, and minimizing for data reuse, the fusion problem has been formulated as a static weighted graph partitioning problem (known as the Weighted Loop Fusion problem). We show that this scheme cannot accurately track data reuse between multiple independent loops, since it overestimates total data reuse of certain cases. Our formulation in terms of partitions allows use of realistic cost functions that can track resource usage accurately. We give correctness proofs, and prove that WSP can maximize data reuse in programs exactly, in contrast to prior work.

In this paper we propose a parallel programming model that combines two well-known execution models: Single Instruction, Multiple Data (SIMD) and Single Program, Multiple Data (SPMD). The combined model supports SIMD-style data parallelism in global address space and supports SPMD-style task parallelism in local address space. One of the most important features in the combined model is that data communication is expressed by global data assignments instead of message passing. We implement this combined programming model into Python, making parallel programming with Python both highly productive and performing on distributed memory multi-core systems. We base the SIMD data parallelism on DistNumPy, an auto-parallel zing version of the Numerical Python (NumPy) package that allows sequential NumPy programs to run on distributed memory architectures. We implement the SPMD task parallelism as an extension to DistNumPy that enables each process to have direct access to the local part of a shared array. To harvest the multi-core benefits in modern processors we exploit multi-threading in both SIMD and SPMD execution models. The multi-threading is completely transparent to the user -- it is implemented in the runtime with Open MP and by using multi-threaded libraries when available. We evaluate the implementation of the combined programming model with several scientific computing benchmarks using two representative multi-core distributed memory systems -- an Intel Nehalem cluster with Infini band interconnects and a Cray XE-6 supercomputer -- up to 1536 cores. The benchmarking results demonstrate scalable good performance.

In this work we present optimizations of a grid-based projector-augmented wave method software, GPAW for the Blue Gene/P architecture. The improvements are achieved by exploring the advantage of shared and distributed memory programming also known as hybrid programming. The work focuses on optimizing a very time consuming operation in GPAW, the finite-different stencil operation, and different hybrid programming approaches are evaluated. The work succeeds in demonstrating a hybrid programming model which is clearly beneficial compared to the original flat programming model. In total an improvement of 1.94 compared to the original implementation is obtained. The results we demonstrate here are reasonably general and may be applied to other finite difference codes.