• Corpus ID: 51796723

The 1st US-Japan Workshop Enabling Global Collaborations in Big Data Research

  title={The 1st US-Japan Workshop Enabling Global Collaborations in Big Data Research},
  author={Masaru Kitsuregawa and Etsuya Shibayama},
Supernovae Type-Ia (SNeIa) play a significant role in exploring the history of the expansion of the Universe, since they are the best-known standard candles with which we can accurately measure the distance to the objects. Finding large samples of SNeIa and investigating their detailed characteristics has become an important issue in cosmology and astronomy. Existing methods relied on a photometric approach that first measures the luminance of supernova candidates precisely and then fits the… 



Lessons Learned from the Analysis of System Failures at Petascale: The Case of Blue Waters

An analysis of failures and their impact for Blue Waters, the Cray hybrid (CPU/GPU) supercomputer at the University of Illinois at Urbana-Champaign, based on both manual failure reports and automatically generated event logs collected over 261 days finds hardware is not the main cause of system downtime.

Towards a Distributed Large-Scale Dynamic Graph Data Store

DegAwareRHH, a high performance dynamic graph data store designed for scaling out to store large, scale-free graphs by leveraging compact hash tables with high data locality, is proposed and extended for multiple processes and distributed memory.

PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs

This paper describes the challenges of computation on natural graphs in the context of existing graph-parallel abstractions and introduces the PowerGraph abstraction which exploits the internal structure of graph programs to address these challenges.

Scaling Techniques for Massive Scale-Free Graphs in Distributed (External) Memory

This work applies an edge list partitioning technique, designed to accommodate high-degree vertices (hubs) that create scaling challenges when processing scale-free graphs, and uses ghost vertices to represent the hubs to reduce communication hotspots.

Genome-wide detection of intervals of genetic heterogeneity associated with complex traits

This work presents an approach that overcomes both problems: it allows one to automatically find all contiguous sequences of single nucleotide polymorphisms in the genome that are jointly associated with the phenotype and solves both the inherent computational efficiency problem and the statistical problem of multiple hypothesis testing, which are both caused by the huge number of candidate intervals.

CloudBB: Scalable I/O Accelerator for Shared Cloud Storage

This work proposes a novel fast, scalable and fault tolerant filesystem called CloudBB (Cloud-based Burst Buffer), which creates an on-demand two-level hierarchical storage system and caches popular files to accelerate I/O performance.

Tensor Balancing on Statistical Manifold

The key to the algorithm is that the gradient of the manifold, used as a Jacobian matrix in Newton's method, can be analytically obtained using the Moebius inversion formula, the essential of combinatorial mathematics.

New Directions for a Japanese Academic Backbone Network

The architectural design and related services of a new Japanese academic backbone network, called SINET5, which will be launched in April 2016, will enable users to leverage evolving cloud-computing powers as well as draw on a high-performance platform for data-intensive applications.

AprioriGWAS, a New Pattern Mining Strategy for Detecting Genetic Variants Associated with Disease through Interaction Effects

AprioriGWAS, a successful method in field of Frequent Itemset Mining (FIM) in which a pattern growth strategy is leveraged to effectively and accurately reduce search space, can efficiently identify genetically associated genotype patterns and test the hypotheses of epistasis.

A framework for service provisioning in virtual sensor networks

The aim of the proposed architecture is to enable the realization of scalable, flexible, adaptive, energy-efficient, and trust-aware VSN platforms, focusing on the reduction of deployment complexity and management cost, and on advanced interoperability mechanisms.