Die-stacked DRAM is a technology that will soon be integrated in high-performance systems. Recent studies have focused on hardware caching techniques to make use of the stacked memory, but these approaches require complex changes to the processor and also cannot leverage the stacked memory to increase the system's overall memory capacity. In this work, we explore the challenges of exposing the stacked DRAM as part of the system's physical address space. This non-uniform access memory (NUMA) styled approach greatly simplifies the hardware and increases the physical memory capacity of the system, but pushes the burden of managing the heterogeneous memory architecture (HMA) to the software layers. We first explore simple (and somewhat impractical) schemes to manage the HMA, and then refine the mechanisms to address a variety of hardware and software implementation challenges. In the end, we present an HMA approach with low hardware and software impact that can dynamically tune itself to different application scenarios, achieving performance even better than the (impractical-to-implement) baseline approaches.

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Abstract-Computers with hardware accelerators, also referred to as hybrid-core systems, speedup applications by offloading certain compute operations that can run faster on accelerators. Thus, it is not surprising that many of top500 supercomputers use accelerators. However, in addition to procurement cost, significant programming and porting effort is required to realize the potential benefit of such accelerators. Hence, before building such a system it is prudent to answer the question 'what is the projected performance benefit from accelerators for the workloads of interest?' We address this question by way of a performance-modeling framework that predicts realizable application performance on accelerators rapidly and accurately without going to the considerable effort of porting and tuning.The modeling framework first automatically identifies commonly found compute patterns in scientific applications which we term idioms, which may benefit by accelerator technology. Next the framework models the predicted speedup of those idioms if they were to be ported to and run on hardware accelerators. As a proof of concept we characterize two kinds of accelerators 1) the FPGA accelerators on a Convey HC-1 system and 2) an NVIDIA FERMI GPU accelerator. We model performance of the idioms gather/scatter and stream and our predictions show that where these occur in two full-scale HPC applications, Milc and HYCOM, gather/scatter speeds up by as much as 15X, and stream by as much as 14X, whereas the overall compute time of Milc improves by 3.4% and HYCOM by 20%. The cost of migrating data to/from the accelerator device can dwarf the benefit of speedup and hence we present models of data migration costs and its impact on the performance of Milc and HYCOM.

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