Here we present NeuroVault—a web based repository that allows researchers to store, share, visualize, and decode statistical maps of the human brain. NeuroVault is easy to use and employs modern web technologies to provide informative visualization of data without the need to install additional software. In addition, it leverages the power of the Neurosynth database to provide cognitive decoding of deposited maps. The data are exposed through a public REST API enabling other services and tools to take advantage of it. NeuroVault is a new resource for researchers interested in conducting meta- and coactivation analyses.

Compiler transformations can significantly improve data locality for many scientific programs. In this paper, we show iterative solvers for partial differential equations (PDEs) in three dimensions require new compiler optimizations not needed for 2D codes, since reuse along the third dimension cannot fit in cachefor larger problem sizes. Tiling is a program transformation compilers can apply to capture this reuse, but successful application of tiling requires selection of non-conflicting tiles and/or padding array dimensions to eliminate conflicts. We present new algorithms and cost models for selecting tiling shapes and array pads. We explain why tiling is rarely needed for 2D PDE solvers, but can be helpful for 3D stencil codes. Experimental results show tiling 3D codes can reduce miss rates and achieve performance improvements of 17-121% for key scientific kernels, including a 27% average improvement for the key computational loop nest in the SPEC/NAS benchmark MGRID.

Here we present NeuroVault -a web based repository that allows researchers to store, share, visualize, and decode statistical maps of the human brain. NeuroVault is easy to use and employs modern web technologies to provide informative visualization of data without the need to install additional software. In addition, it leverages the power of the Neurosynth database to provide cognitive decoding of deposited maps. The data are exposed through a public REST API enabling other services and tools to take advantage of it. NeuroVault is a new resource for researchers interested in conducting meta and coactivation analyses.

Abstract. Linear algebra codes contain data locality which can be exploited by tiling multiple loop nests. Several approaches to tiling have been suggested for avoiding conflict misses in low associativity caches. We propose a new technique based on intra-variable padding and compare its performance with existing techniques. Results show padding improves performance of matrix multiply by over 100% in some cases over a range of matrix sizes. Comparing the efficacy of different tiling algorithms, we discover rectangular tiles are slightly more efficient than square tiles. Overall, tiling improves performance from 0-250%. Copying tiles at run time proves to be quite effective.

NeuroVault.org is dedicated to storing outputs of analyses in the form of statistical maps, parcellations and atlases, a unique strategy that contrasts with most neuroimaging repositories that store raw acquisition data or stereotaxic coordinates. Such maps are indispensable for performing meta-analyses, validating novel methodology, and deciding on precise outlines for regions of interest (ROIs). NeuroVault is open to maps derived from both healthy and clinical populations, as well as from various imaging modalities (sMRI, fMRI, EEG, MEG, PET, etc.). The repository uses modern web technologies such as interactive web-based visualization, cognitive decoding, and comparison with other maps to provide researchers with efficient, intuitive tools to improve the understanding of their results. Each dataset and map is assigned a permanent Universal Resource Locator (URL), and all of the data is accessible through a REST Application Programming Interface (API). Additionally, the repository supports the NIDM-Results standard, and has the ability to parse outputs from popular FSL and SPM software packages to automatically extract relevant metadata. This ease of use, modern web-integration, and pioneering functionality holds promise to improve the workflow for making inferences about and sharing whole-brain statistical maps.

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Many cache misses in scientific programs are due to conflicts caused by limited set associativity. Two data-layout transformations, inter-and intra-variable padding, can eliminate many confict misses at compile time. We present GROUP-PAD, an inter-variable padding heuristic to preserve group reuse in stencil computations frequently found in scientific computations.We show padding can also improve performance in parallel programs.Our optimizations have been implemented and tested on a collection of kernels and programs for different cache and data sizes. Preliminary results demonstrate GROUPPAD is able to consistently preserve group reuse among the programs evaluated, though execution time improvements are small for actual problem and cache sizes tested.Padding improves performance of parallel versions of programs approximately the same magnitude as sequential versions of the same program.

Compiler transformations can significantly improve data locality of scientific programs. In this paper, we examine the impact of multi-level caches on data locality optimizations. We find nearly all the benefits can be achieved by simply targeting the L1 (primary) cache. Most locality transformations are unaffected because they improve reuse for all levels of the cache; however, some optimizations can be enhanced. Inter-variable padding can take advantage of modular arithmetic to eliminate conflict misses and preserve group reuse on multiple cache levels. Loop fusion can balance increasing group reuse for the L2 (secondary) cache at the expense of losing group reuse at the smaller L1 cache. Tiling for the L1 cache also exploits locality available in the L2 cache. Experiments show enhanced algorithms are able to reduce cache misses, but performance improvements are rarely significant. Our results indicate existing compiler optimizations are usually sufficient to achieve good performance for multi-level caches.