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Daniel Bozinov on "Challenges in Genomic Cloud Computing" at the 2012 Sequencing, Finishing, Analysis in the Future Meeting held June 5-7, 2012 in Santa Fe, New Mexico.
The Community Earth System Model (CESM) is a fully-coupled, global climate model that provides state-of-the-art computer simulations of the Earth's past, present, and future climate states. This document provides the agenda and list of participants for the conference. Web materials for all lectures and practical sessions available from: http://www.cesm.ucar.edu/events/tutorials/073012/ .
Population-based central cancer registries collect valuable structured and unstructured data primarily for cancer surveillance and research, enhancing insights into clinical features associated with cancer occurrence, cancer treatment, and cancer outcomes to guide interventions which reduce the cancer burden. Cancer registries primarily collect data on (1) cancer type (case or tumor); (2) patient demographics such as age, gender, and residential address at time of diagnosis; (3) planned first course of treatment; and (4) date of last contact, vital status, and cause of death. Cancer registry data is dynamic, structured data, which is extracted from many unstructured sources such as electronic healthcare records, and consolidated for reporting and other purposes. While available advanced analytic tools such as SEER*Stat have the ability to build SAS queries, we, however, explore an innovative knowledge graph approach to organizing cancer registry data for advanced analytics and visualization, which has unique advantages over approaches of existing tools. This innovative knowledge graph approach semantically enriches the data and easily enables linkage with third-party data, which can better explain variation in outcomes. We have developed a prototype knowledge graph based on data from the Louisiana Tumor Registry and other publicly available datasets including Behavioral Risk Factor Surveillance System, Clinical Trials, DBpedia, GeoNames, Rural-Urban Continuum Codes, and Semantic MEDLINE. The resource description framework (RDF) data model was selected to represent our knowledge graph, which contains more than 25 billion triples and is ~4TB in storage size. To exhibit the benefits of the knowledge graph approach, we used scenario specific queries, which find the relationships between cancer treatment sequences and outcomes. To illustrate its ease of use in iterative analysis, the knowledge graph was linked to external datasets for performing complex queries across multiple datasets. In addition, we used knowledge graphs to identify data discrepancies and to handle schema changes. Finally, we visualized the knowledge graph to discover data patterns. Our results demonstrate this graph-based solution enables cancer researchers to execute complex queries and more easily perform iterative analyses to improve understanding of cancer registry data. In the future, we would like to use high-performance computing (HPC) resources for faster-generating hypotheses with clinical potential from our knowledge graph.
Mobility in America is changing fast. To understand where it’s headed, Argonne caught up with Michael Berube, the acting deputy assistant secretary for transportation in the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy. While at the laboratory, Berube took time to highlight the ways in which technology and consumer demand are shaping transportation, and the insights national laboratories like Argonne can provide about the future.
Today the LHC offline computing relies heavily on CPU resources, despite the interest in compute accelerators, such as GPUs, for the longer term future. The number of cores per CPU socket has continued to increase steadily, reaching the levels of 64 cores (128 threads) with recent AMD EPYC processors, and 128 cores on Ampere Altra Max ARM processors. Over the course of the past decade, the CMS data processing framework, CMSSW, has been transformed from a single-threaded framework into a highly concurrent one. The first multithreaded version was brought into production by the start of the LHC Run 2 in 2015. Since then, the framework's threading efficiency has gradually been improved by adding more levels of concurrency and reducing the amount of serial code paths. The latest addition was support for concurrent Runs. In this work we review the concurrency model of the CMSSW, and measure its scalability with real CMS applications, such as simulation and reconstruction, on mode rn many-core machines. We show metrics such as event processing throughput and application memory usage with and without the contribution of I/O, as I/O has been the major scaling limitation for the CMS applications.
The Mobile Plutonium Facility (MPF) is a portable module design facility that can be deployed anywhere in the world for the safe recovery, packaging, and shipment of a nations legacy, or excess plutonium stockpile. All of the modules of MPF have specific purposes for operating in a mission if one were to ever come up. For my past four internships here at SRNL I have been working on the support of the X-Ray module for MPF. This year I installed new equipment I had purchased last year for the X-Ray Module and made sure the module was mission ready for the chance that the team deploys in the future for plutonium recovery. Using digital radiography (DR) and computed tomography (CT) provides the MPF team the ability to verify the contents of containers that would be holding material. This also ensures the safety of the team members in the glovebox facilities by verifying that there are no sharps, liquids, or other items that may harm the workers or glovebox operations when working with radioactive material. X-Ray imaging can also pick up low density objects like plastic bags and tape. Note how the DR image hides the puncture in the inner can while the CT image shows the damage. New DR and CT computers with upgraded software, New imaging panel, NOVO Portable X-Ray System. Because of the new upgrades to the X-Ray module system containers can be scanned and evaluated quicker than before. With the last system the 3D model reconstruction alone would take anywhere from 10-12 minutes, now with the new system that time has been brought down to just shy of 1 minute 30 seconds. The new portable system also can 1,2 or 4 images of an object and stitch them together into a mosaic for scanning of larger containers.
Nuclear energy is a major contributor to base load electrical power around the world and is predicted to continue to expand in the coming years to meet the increasing global demand for electricity. As new reactors are being constructed to meet these demands, it is imperative to make power plants intrinsically safer and more cost efficient. A major consideration to improving safety and performance is the enhancement of current ceramic nuclear fuels. By using an inert matrix fuel, additional phases can be added to the fuel to increase both the thermal and mechanical properties of the fuel at room and reactor conditions. In this study, multiphase ceramics and the individual single-phase components, will be investigated to understand the effects of multiple phases, various grain sizes, and interfaces on the temperature dependent thermal conductivity. A coupled approach involving both experimental measurements and computational modeling will give insight into the development of future models for understanding the role interfaces play in thermal transport within multiphase systems. (authors)
Purpose: Microbeam Radiation Therapy is a preclinical method in radiation oncology that modulates radiation fields on a micrometre scale. Dose calculation is challenging due to arising dose gradients and therapeutically important dose ranges. Monte Carlo (MC) simulations, often used as gold standard, are computationally expensive and hence too slow for the optimisation of treatment parameters in future clinical applications. On the other hand, conventional kernel based dose calculation leads to inaccurate results close to material interfaces. The purpose of this work is to overcome these inaccuracies while keeping computation times low. Methods: A point kernel superposition algorithm is modified to account for tissue inhomogeneities. Instead of conventional ray tracing approaches, methods from differential geometry are applied and the space around the primary photon interaction is locally warped. The performance of this approach is compared to MC simulations and a simple convolution algorithm (CA) for two different phantoms and photon spectra. Results: While peak doses of all dose calculation methods agreed within less than 4% deviations, the proposed approach surpassed a simple convolution algorithm in accuracy by a factor of up to 3 in the scatter dose. In a treatment geometry similar to possible future clinical situations differences between Monte Carlo and the differential geometry algorithm were less than 3%. At the same time the calculation time did not exceed 15 minutes. Conclusion: With the developed method it was possible to improve the dose calculation based on the CA method with respect to accuracy especially at sharp tissue boundaries. While the calculation is more extensive than for the CA method and depends on field size, the typical calculation time for a 20×20 mm{sup 2} field on a 3.4 GHz and 8 GByte RAM processor remained below 15 minutes. Parallelisation and optimisation of the algorithm could lead to further significant calculation time reductions.
In a quasiclassical trajectory simulation, the vibrational modes are initialised with quantised vibrational energies, but vibrational phases are sampled by Monte Carlo. This requires an algorithm to assign coordinates and momenta to the various atoms. In this work, we present two methods for implementing this for nonrotating polyatomic molecules, namely, fixed-energy vibrational-state-selected initial conditions and thermal initial conditions. We also present a method for initiating classical trajectories with a ground-state Wigner distribution. These vibrational treatments are sufficient to initialise trajectories for unimolecular processes, and we also show how they can be applied to simulate bimolecular collision processes. The treatments of unimolecular and bimolecular collision processes are available in two Python codes called wigner_state_selected.py and bimolecular_collision.py, respectively, which will generate initial condition files that are recognisable by the SHARC and SHARC-MN computer programs for dynamics calculations. Both codes are available as standalone programs, as well as being included in SHARC-MN, and they will be included in future versions of SHARC. Here, the methods implemented in these codes are mostly also available in the ANT computer program, and those that are not available in ANT will be incorporated in future versions of ANT.
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX’s tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.
As part of its national security science mission, Los Alamos National Laboratory and HPC have a long, entwined history dating back to the earliest days of computing. From bringing the first problem to the nation’s first computer to building the first machine to break the petaflop barrier, Los Alamos holds many “firsts” in HPC breakthroughs. Today, supercomputers are integral to stockpile stewardship and the Laboratory continues to work with vendors in developing the future of HPC.
Detector simulation is consuming at least half of the HEP computing cycles, and even so, experiments have to take hard decisions on what to simulate, as their needs greatly surpass the availability of computing resources. New experiments still in the design phase such as FCC, CLIC and ILC as well as upgraded versions of the existing LHC detectors will push further the simulation requirements. Since the increase in computing resources is not likely to keep pace with our needs, it is therefore necessary to explore innovative ways of speeding up simulation in order to sustain the progress of High Energy Physics. The GeantV project aims at developing a high performance detector simulation system integrating fast and full simulation that can be ported on different computing architectures, including CPU accelerators. After more than two years of R&D the project has produced a prototype capable of transporting particles in complex geometries exploiting micro-parallelism, SIMD and multithreading. Portability is obtained via C++ template techniques that allow the development of machine- independent computational kernels. Furthermore, a set of tables derived from Geant4 for cross sections and final states provides a realistic shower development and, having been ported into a Geant4 physics list, can be used as a basis for a direct performance comparison.
The final review for the FY21 Advanced Simulation and Computing (ASC) Computational Systems and Software Environments (CSSE) L2 Milestone #7840 was conducted on August 25th, 2021 at Sandia National Laboratories in Albuquerque, New Mexico. The review committee/panel unanimously agreed that the milestone has been successfully completed, exceeding expectations on several of the key deliverables.
Our computation effort is primarily concentrated in support of current and future measurements being carried out at various synchrotron radiation facilities around the globe and for charge transfer experiments. In our work we solve the Schrödinger or Dirac equation for the appropriate collision problem using the R-matrix or R-matrix with pseudo-states approach from first principles. The time dependent lattice (TDL) method is also used in our work on charge exchange. A brief summary of the methodology and ongoing developments implemented in the R-matrix suite of Breit-Pauli and Dirac-Atomic R-matrix codes (DARC) is presented. We illustrate vividly the sophistication level of large scale petaflop computations necessary to model accurately the spectra currently being measured at various synchrotron radiation facilities. The new Cray XC40 architecture installed at HLRS is playing a vital role in our computational effort.
One of the objectives of the high temperature design methodology activities is to develop and validate both improvements and the basic features of ASME Boiler and Pressure Vessel Code, Section III, Rules for Construction of Nuclear Facility Components, Division 5, High Temperature Reactors, Subsection HB, Subpart B (HBB). The overall scope of this task is to develop a computer program to aid assessment procedures of components under specified loading conditions in accordance with the elevated temperature design requirements for Division 5 Class A components. There are many features and alternative paths of varying complexity in HBB. The initial focus of this computer program is a basic path through the various options for a single reference material, 316H stainless steel. However, the computer program is being structured for eventual incorporation all of the features and permitted materials of HBB. This report will first provide a description of the overall computer program, particular challenges in developing numerical procedures for the assessment, and an overall approach to computer program development. This is followed by a more comprehensive appendix, which is the draft computer program manual for the program development. The strain limits rules have been implemented in the computer program. The evaluation of creep-fatigue damage will be implemented in future work scope.
Because most technology and computer architecture innovations were (intentionally) invisible to higher layers, application and other software developers could reap the benefits of this progress without engaging in it. Higher performance has both made more computationally demanding applications feasible (e.g., virtual assistants, computer vision) and made less demanding applications easier to develop by enabling higher-level programming abstractions (e.g., scripting languages and reusable components). Improvements in computer system cost-effectiveness enabled value creation that could never have been imagined by the field's founders (e.g., distributed web search sufficiently inexpensive so as to be covered by advertising links). The wide benefits of computer performance growth are clear. Recently, Danowitz et al. apportioned computer performance growth roughly equally between technology and architecture, with architecture credited with ~80x improvement since 1985. As semiconductor technology approaches its "end-of-the-road" (see below), computer architecture will need to play an increasing role in enabling future ICT innovation. But instead of asking, "How can I make my chip run faster?," architects must now ask, "How can I enable the 21st century infrastructure, from sensors to clouds, adding value from performance to privacy, but without the benefit of near-perfect technology scaling?". The challenges are many, but with appropriate investment, opportunities abound. Underlying these opportunities is a common theme that future architecture innovations will require the engagement of and investments from innovators in other ICT layers.
This report gives an account of the 2017 achievements and findings of the ALCF as well as future directives.