The MGHPCC Research Committee
The MGHPCC research committee is made up of representatives from each of the partner universities. It is chaired by Chris Hill (MIT) and David Kaeli (Northeastern). You can contact the committee at firstname.lastname@example.org
Massachusetts research universities are world leaders in computational research for physical science, life science, computer science and many other disciplines. In the past, computationally-intensive research in New England was often constrained by cost and construction lead times for data center space on individual university campuses. The MGHPCC has removed that constraint, and has brought computing and storage for numerous research groups close to each other, opening new opportunities for collaboration.
The MGHPCC research committee was formed to maximize the impact of the facility on current research, support the new collaborations that the facility makes possible, and encourage submission of large, joint proposals for acquiring and exploiting state-of-the-art research infrastructure
The committee also sponsors cross-institutional seminars and workshops, works with faculty and staff at all levels in partner organizations to realize its goals, and works closely with the MGHPCC education and outreach committee.
Computationally intensive Research in the MGHPCC Consortium
High-performance computing (HPC) systems that bring together large numbers of CPUs and massive amounts of storage, are playing a transformative role in research. For example, high performance computers are used to create, simulate and solve models of complex phenomena that range from protein structure, to turbulent fluid flows, to the dynamics of the earth’s atmosphere, to pathways of human social interaction, to galactic evolution. These models provide new perspectives and new ways of making discoveries. The following is a small sample of the computationally-intensive research being conducted at the MGHPCC universities. Clicking on our RESEARCH tag gives a more detailed view of current activity.
FloDesign Sonics is using MGHPCC to understand micron sized particles’ movement inside 3D acoustic fields. Such types of calculations involve solving trajectories of tens of thousands of particles and their effect on the flow field along with particle-particle collisions. Due to the complex nature of such computations, a supercomputer like MHGPCC is an ideal resource. The resulting data helps them understand the physics behind acoustical trapping, clustering and separation of particles from the bulk fluid and the type of acoustic field and geometry required for a more efficient process. This helps in optimizing their systems and aids product development. MORE
Designing Secure Computer Systems
Cyber security and the design of systems to protect data from attack by malicious entities, presents an enormous challenge. Computer architect Srini Devedas, a professor in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT, collaborates with software researchers at MIT and other universities, to meld secure architecture with secure compiler and operating system software, to protect data from the ground up MORE
Modern Neuroscience demands Big Data
Scientists at Harvard University are using the MGHPCC for their research to uncover the mysteries of the brain. Whether studying the connection and wiring of individual neurons at the micro-scale to exploring the interaction of brain areas across thousands of people using magnetic resonance imaging technologies at the macro-scale, either way, progress is predicated on efficient handling of big data. MORE
Modeling electron excitation in organic photovoltaic materials
Theoretical chemist Adam Willard, Assistant Professor at the Massachusetts Institute of Technology, and his group are using the MGHPCC facility in their computer modeling work, which seeks to understand the molecular-scale behavior of photonically excited-electrons in organic photovoltaic materials, which can be used to build thin-film solar panels made out of semi-conducting plastic MORE
A project team led by John Kovac, Associate Professor of Astronomy and Physics and member of the Harvard-Smithsonian Center for Astrophysics, recently announced detection of B-mode polarization and gravitational waves, providing insight into the universe’s first moments after the Big Bang. Albert Einstein’s general theory of relativity hypothesized the existence of gravitational waves, but until now they were never physically observed. The project used a telescope at the South Pole to examine Cosmic Microwave Background, the oldest light in the universe. To analyze the data, the project used 5.1 million CPU hours on computers operated by the Harvard Research Computing Group. MORE
ATLAS Northeast Tier 2 center.
ATLAS is a particle physics experiment at the Large Hadron Collider at CERN that is searching for new discoveries in the head-on collisions of protons of extraordinarily high energy. ATLAS will learn about the basic forces that have shaped our Universe since the beginning of time and that will determine its fate. ATLAS was one of the two experiments that recently observed the Higgs boson. The MGHPCC houses the computing facilities used by researchers at BU and Harvard involved with the project MORE
Toward a Smarter Greener Grid
Building a Smarter, Greener Grid. UMass Amherst computer scientist Prashant Shenoy and electrical and computer engineer David Irwin are leading a team of researchers focused on analyzing smart meters and other tools that could transform the way energy is utilized, monitored, and controlled in America’s buildings, which currently comprise 75% of the national electric bill. Along with partner Holyoke Gas and Electric (HG&E), Shenoy and his team are analyzing data from 18,000 smart meters in the city of Holyoke. The analysis will be used by projects that include design of smarter thermostats, a web-based solar predictor, and an electrical reserve battery. MORE
Strength and Fracture Mechanisms of Hierarchical Biological Materials.
Alain Karma (Northeastern), and Markus Buehler (MIT) are using high performance computers in a multiscale modeling effort that incorporates length-scales from the atom to the structural scale to explore and test how biological materials like bone or nacre can, despite their apparent fragility, resist breakage. The multiscale computational approach developed in this project aims to permit an exploration of fundamental mechanisms that control the strength of biological materials, and at the same time, create a platform for the biomimetic (literally nature mimicking) design of nanostructured composite engineering materials for structural and medical applications. MORE
Science and Policy of Global Change.
Understanding the complex, long-term changes in our land, air and water requires breakthroughs in measurement, modeling and prediction. Responding to these changes requires innovative policies that comprehend agriculture, energy needs, trade and finance — along with the political and communications savvy to organize a genuinely global approach. The Joint Program on the Science and Policy of Global Change is MIT’s response to these research, analysis, and public education challenges. At the heart of much of the Program’s work lies MIT’s Integrated Global System Model (IGSM), a linked set of computer models designed to simulate the global environmental changes that arise as a result of human causes. In this way, it explores the interplay between Earth systems and human activity. MORE
Multi-Scale Ocean Modeling
An MGHPCC seed fund award allowed Professor Pierre Lermusiaux and collaborators to develop multi-scale models of the marine environment off the New England coast designed to talk to one-another and interface with real-time observations MORE
Designing Cloud and Big Data Platforms for Scientific and HPC Applications.
Orran Krieger (BU), Peter Desnoyers (Northeastern), Manuel Garber (UMass Worcester), and Prashant Shenoy (UMass Amherst) are exploring ways to better tailor today’s cloud and big data platforms for Big Data – the massive volume of information so many disciplines from the life-sciences, through the physical-sciences, to the social sciences now generate. With an eye to genomics and sequencing applications in the life-sciences, real-time data mining of smart grids, even weather nowcasting (using weather data to develop short-term personalized forecasts), the team is looking at ways to re-architect big data processing methods and cloud platforms to better meet the needs of data intensive scientific applications. MORE
Informing Economic Policy Through Computation.
The Chetty research group, which is part of the Lab for Economic Applications and Policy (LEAP) at Harvard, focuses on understanding how to improve government policy to spark economic growth and reduce poverty. The group analyzes the effects of government policies using mathematical models of the economy and tests these models using large datasets on households and firms. Current questions include the long term economic effects of tax policies and government spending, the effect of increased transparency and salience on anti-poverty programs, and the economic effects of changing social norms. These theoretical and empirical research projects use high performance computers operated by the Harvard Research Computing Group to run complex dynamic models and analyze datasets that contain billions of observations. MORE
Robert Fisher, a professor of astrophysics at UMass Dartmouth, uses high performance computing to model and make predications about the behavior of supernovae. Supernovae are the incredible explosions that take place at the end of a star’s life cycle, and are among the brightest objects in the sky. They have been an object of fascination for astronomers and astrophysicists for many centuries. Fisher models their behavior using large computer simulations which simultaneously solve the complex physics equations that make up a detailed theoretical description of a star or star pair. MORE
Exploring the Dark Matter Substructure of Milky Way Galaxies.
Anna Frebel (MIT), Edmund Bertschinger (MIT), and Lars Hernquist (Harvard) are using astronomical observations and the fundamental laws of physics to improve our understanding of how galaxies assemble, their origins, and what controls their characteristics. With the advent of computers, astrophysicists have been able to build increasingly sophisticated models of the growth of agglomerations such as the Milky Way through so-called N-body simulations. In this project, the investigators seek to leverage High Performance Computing to allow them to run not one but multiple N-body problems in parallel, enabling them to apply statistical approaches to uncovering the underlying physical processes at work. It is hoped this will lead to the conclusive confirmation of the details of the assembly history of the Milky Way as well as a better understanding of general galaxy behavior. MORE
Automated real-time medical imaging analysis.
Patricia Ellen Grant (Harvard Medical School & Children’s Hospital Boston), and Jonathan Appavoo (Boston University) are using high-performance computing to make real-time radiological image analysis easier and less costly to use. MORE
Organic Light Harvesting Antennas.
Typically plants need an average of at least a few hundred watts per square meter of solar radiation to survive. At depths of a few hundred meters down in the ocean, the amount of solar radiation decays to around a millionth of the surface intensity so that photosynthesizing organisms at such depths only receive 10-4 Wm-2 of radiation. MGHPCC seed fund awardees Alán Aspuru-Guzik (Quantum Chemistry, Harvard) and Alfredo Alexander-Katz (Materials Science, MIT) are studying the properties of a photosynthetic bacteria (Chlorobium Tepidum, also known as Green sulphur bacteria) that manages to survive in this low energy environment. Their work seeks to understand the interplay between biomolecular structure and efficient light harvesting on the quantum scale and how this could lead to radical new approaches to energy generation. MORE
Computational Identification of Outcome-Associated DNA Alterations in Neuroblastoma
Stefano Monti (BU) and Roberto Chiarlie (Harvard) are investigating ways to improve the effectiveness of treatments for Neuroblastoma, one of the most common tumor-causing cancers in children. Clinically, neuroblastoma is classified in 4 stages, with increasingly poor prognosis. However a significant subset of stage 4 patients (the most advanced stage) can be cured and may even regress spontaneously. The goal of this study is to develop high-throughput sequencing techniques to identify novel DNA markers allowing the two prognostic groups to be distinguished, informing subsequent treatment decisions.
Genome-scale Characterization of Chromosonal Aberrations Using Parallelizable Compression Algorithms.
Dan Simovici (UMass Boston), Nurit Haspel (UMass Boston), David Weisman (UMass Boston), and Jennifer Rosen (BU) are seeking to speed up detection of genetic mutations that cause cancer. One of the great difficulties in developing effective cancer treatments is the stochastic or statistically varying nature of these mutations – each patient and each tumor may present a unique genomic signature. As individual full-genome sequencing comes within reach, this research will develop new data compression techniques (essentially automatically locating and leveraging repetitive sequences) for large-scale data mining of next-generation DNA sequencing data to speed-up detection of large chromosomal aberrations.
Automated Segmentation of Vessel Network Structures in Large Image Stack Sets.
Deniz Erdogmus (Northeastern) Jeff Lichtman (Harvard) and David Boas (Harvard/Mass. General Hospital) are developing software that will improve our understanding of the complex behavior of neuronal circuits, and how they evolve during synaptic competition. Today neuroscientists must manually construct the tree-structured nerves from 3D microscopy imagery. Manual reconstruction of a single cell may take weeks to months depending on its type and complexity. In this project, the team seeks to develop an open high-performance automatic image analysis software package to massively accelerate this process, making it feasible to analyze automatically or interactively a large imagery database for quantitative modeling and statistical analysis.
Hossein Mosallaei and David Kaeli (Northeastern) have teamed up with Efthimios Kaxiras (Harvard) to create computer models that simulate the behavior of metals, dielectric and magnetic particles at extremely small scales, allowing insights into the behavior of important new materials.
Greening High Performance Computing
Ayse K. Coskun (BU) Martin C. Herbordt (BU), and Gunar Schirner (Northeastern) are exploring ways to measure and improve the operating cost and energy efficiency of large-scale high-performance computing systems by taking a holistic view of user demand, server power consumption, and facility infrastructure along with energy market features and constraints.
Massive Genomic Data Processing and Deep Analysis.
Yanlei Diao and Li-Jun Ma (UMass Amherst), together with Samuel Madden (MIT), and Yiping Shen and Bai-Lin Wu (Harvard Medical School & Children’s Hospital Boston) are developing a next-generation, on-demand service for managing and processing massive amounts of genome information and automating the analysis of association, causality, and outlier detection. MORE
Making matrix calculations 1000 times faster
Lorena Barba (Boston University), Cris Cecka (Harvard) and Hans Johnston (UMass Amherst) are developing computational techniques to accelerate matrix calculations (a fundamental building block for many High Performance Computing applications) in future generation “exascale” software platforms, which will be 1,000 times faster than current computing speeds.