This report captures and expands the outcomes of this workshop. In the context of extreme heterogeneity, it defines basic research needs and opportunities in computer science research to develop smart and trainable operating and runtime systems, programming environments, and predictive tools that will make future systems easier to adapt to scientists' computing needs and easier for facilities to deploy securely.

Opportunities offered by new neuro-technologies are threatened by lack of coherent plans to analyze, manage, and understand the data. High-performance computing will allow exploratory analysis of massive datasets stored in standardized formats, hosted in open repositories, and integrated with simulations.

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To achieve exascale computing, fundamental hardware architectures must change. The most significant consequence of this assertion is the impact on the scientific applications that run on current high performance computing (HPC) systems, many of which codify years of scientific domain knowledge and refinements for contemporary computer systems. In order to adapt to exascale architectures, developers must be able to reason about new hardware and determine what programming models and algorithms will provide the best blend of performance and energy efficiency into the future. While many details of the exascale architectures are undefined, an abstract machine model is designed to allow application developers to focus on the aspects of the machine that are important or relevant to performance and code structure. These models are intended as communication aids between application developers and hardware architects during the co-design process. We use the term proxy architecture to describe a parameterized version of an abstract machine model, with the parameters added to elucidate potential speeds and capacities of key hardware components. These more detailed architectural models are formulated to enable discussion between the developers of analytic models and simulators and computer hardware architects. They allow for application performance analysis and hardware optimization opportunities. In this report our goal is to provide the application development community with a set of models that can help software developers prepare for exascale. In addition, use of proxy architectures, through the use of proxy architectures, we can enable a more concrete exploration of how well application codes map onto the future architectures.

Reverse Time Migration (RTM) has become the standard for high-quality imaging in the seismic industry. RTM relies on PDE solutions using stencils that are 8 t h order or larger, which require large-scale HPC clusters to meet the computational demands. However, the rising power consumption of conventional cluster technology has prompted investigation of architectural alternatives that offer higher computational efficiency. In this work, we compare the performance and energy efficiency of three architectural alternatives -the Intel Nehalem X5530 multicore processor, the NVIDIA Tesla C2050 GPU, and a general-purpose manycore chip design optimized for high-order wave equations called "Green Wave." We have developed an FPGA-accelerated architectural simulation platform to accurately model the power and performance of the Green Wave design. Results show that across highly-tuned high-order RTM stencils, the Green Wave implementation can offer up to 8× and 3.5× energy efficiency improvement per node respectively, compared with the Nehalem and GPU platforms. These results point to the enormous potential energy advantages of our hardware/software co-design methodology.