Over the last twenty years, the open source community has provided more and more software on which the world's High Performance Computing (HPC) systems depend for performance and productivity. The community has invested millions of dollars and years of effort to build key components. But although the investments in these separate software elements have been tremendously valuable, a great deal of productivity has also been lost because of the lack of planning, coordination, and key integration of technologies necessary to make them work together smoothly and efficiently, both within individual PetaScale systems and between different systems. It seems clear that this completely uncoordinated development model will not provide the software needed to support the unprecedented parallelism required for peta/exascale computation on millions of cores, or the flexibility required to exploit new hardware models and features, such as transactional memory, speculative execution, and GPUs. This report describes the work of the community to prepare for the challenges of exascale computing, ultimately combing their efforts in a coordinated International Exascale Software Project.

The research literature to date mainly aimed at reducing energy consumption in HPC environments. In this paper we propose a job power aware scheduling mechanism to reduce HPC's electricity bill without degrading the system utilization. The novelty of our job scheduling mechanism is its ability to take the variation of electricity price into consideration as a means to make better decisions of the timing of scheduling jobs with diverse power profiles. We verified the effectiveness of our design by conducting trace-based experiments on an IBM Blue Gene/P and a cluster system as well as a case study on Argonne's 48-rack IBM Blue Gene/Q system. Our preliminary results show that our power aware algorithm can reduce electricity bill of HPC systems as much as 23%.

There is growing concern that I/O systems will be hard pressed to satisfy the requirements of future leadership-class machines. Even current machines are found to be I/O bound for some applications. In this paper, we identify existing performance bottlenecks in data movement for I/O on the IBM Blue Gene/P (BG/P) supercomputer currently deployed at several leadership computing facilities. We improve the I/O performance by exploiting the network topology of BG/P for collective I/O, leveraging data semantics of applications and incorporating asynchronous data staging. We demonstrate the efficacy of our approaches for synthetic benchmark experiments and for application-level benchmarks at scale on leadership computing systems.

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Large high-resolution displays are becoming increasingly common in research settings, providing data scientists with visual interfaces for the analysis of large datasets. Numerous studies have demonstrated unique perceptual and cognitive benefits afforded by these displays in visual analytics and information visualization tasks. However, the effects of these displays on knowledge discovery in exploratory visual analysis are still poorly understood. We present the results of a small-scale study to better understand how display size and resolution affect insight. Analyzing participants' verbal statements, we find preliminary evidence that larger displays with more pixels can significantly increase the number of discoveries reported during visual exploration, while yielding broader, more integrative insights. Furthermore, we find important differences in how participants performed the same visual exploration task using displays of varying sizes. We tie these results to extant work and propose explanations by considering the cognitive and interaction costs associated with visual exploration.

Constructing integrative visualizations that simultaneously cater to a variety of data types is challenging. Hybrid-reality environments blur the line between virtual environments and tiled display walls. They incorporate high-resolution, stereoscopic displays, which can be used to juxtapose large, heterogeneous datasets while providing a range of naturalistic interaction schemes. They thus empower designers to construct integrative visualizations that more effectively mash up 2D, 3D, temporal, and multivariate datasets.

Understanding the nature of turbulent flows remains one of the outstanding questions in classical physics. Significant progress has been recently made using computer simulation as an aid to our understanding of the rich physics of turbulence. Here, we present both the computer science and the scientific features of a unique terascale simulation of a weakly compressible turbulent flow that includes tracer particles. (Terascale refers to performance and dataset storage use in excess of a teraflop and terabyte, respectively.) The simulation was performed on the Lawrence Livermore National Laboratory IBM Blue Gene/Le system, using version 3 of the FLASH application framework. FLASH3 is a modular, publicly available code designed primarily for astrophysical simulations, which scales well to massively parallel environments. We discuss issues related to the analysis and visualization of such a massive simulation and present initial scientific results. We also discuss challenges related to making the database available for public release. We suggest that widespread adoption of an open dataset model of high-performance computing is likely to result in significant advantages for the scientific computing community, in much the same way that the widespread adoption of open-source software has produced similar gains over the last 10 years.

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