Publications
Streaming graph challenge: stochastic block partition
Summary
Summary
An important objective for analyzing real-world graphs is to achieve scalable performance on large, streaming graphs. A challenging and relevant example is the graph partition problem. As a combinatorial problem, graph partition is NP-hard, but existing relaxation methods provide reasonable approximate solutions that can be scaled for large graphs. Competitive...
A linear algebra approach to fast DNA mixture analysis using GPUs
Summary
Summary
Analysis of DNA samples is an important step in forensics, and the speed of analysis can impact investigations. Comparison of DNA sequences is based on the analysis of short tandem repeats (STRs), which are short DNA sequences of 2-5 base pairs. Current forensics approaches use 20 STR loci for analysis...
Benchmarking data analysis and machine learning applications on the Intel KNL many-core processor
Summary
Summary
Knights Landing (KNL) is the code name for the second-generation Intel Xeon Phi product family. KNL has generated significant interest in the data analysis and machine learning communities because its new many-core architecture targets both of these workloads. The KNL many-core vector processor design enables it to exploit much higher...
Static graph challenge: subgraph isomorphism
Summary
Summary
The rise of graph analytic systems has created a need for ways to measure and compare the capabilities of these systems. Graph analytics present unique scalability difficulties. The machine learning, high performance computing, and visual analytics communities have wrestled with these difficulties for decades and developed methodologies for creating challenges...
Performance measurements of supercomputing and cloud storage solutions
Summary
Summary
Increasing amounts of data from varied sources, particularly in the fields of machine learning and graph analytics, are causing storage requirements to grow rapidly. A variety of technologies exist for storing and sharing these data, ranging from parallel file systems used by supercomputers to distributed block storage systems found in...
Learning by doing, High Performance Computing education in the MOOC era
Summary
Summary
The High Performance Computing (HPC) community has spent decades developing tools that teach practitioners to harness the power of parallel and distributed computing. To create scalable and flexible educational experiences for practitioners in all phases of a career, we turn to Massively Open Online Courses (MOOCs). We detail the design...
Benchmarking SciDB data import on HPC systems
Summary
Summary
SciDB is a scalable, computational database management system that uses an array model for data storage. The array data model of SciDB makes it ideally suited for storing and managing large amounts of imaging data. SciDB is designed to support advanced analytics in database, thus reducing the need for extracting...
Enhancing HPC security with a user-based firewall
Summary
Summary
High Performance Computing (HPC) systems traditionally allow their users unrestricted use of their internal network. While this network is normally controlled enough to guarantee privacy without the need for encryption, it does not provide a method to authenticate peer connections. Protocols built upon this internal network, such as those used...
Designing a new high performance computing education strategy for professional scientists and engineers
Summary
Summary
For decades the High Performance Computing (HPC) community has used web content, workshops and embedded HPC scientists to enable practitioners to harness the power of parallel and distributed computing. The most successful approaches, face-to-face tutorials and embedded professionals, don't scale. To create scalable, flexible, educational experiences for practitioners in all...
D4M and large array databases for management and analysis of large biomedical imaging data
Summary
Summary
Advances in medical imaging technologies have enabled the acquisition of increasingly large datasets. Current state-of-the-art confocal or multi-photon imaging technology can produce biomedical datasets in excess of 1 TB per dataset. Typical approaches for analyzing large datasets rely on downsampling the original datasets or leveraging distributed computing resources where small...