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An interactive, container-based workflow tool for creating and spawning directed acyclic graphs (DAGs) of operators in a distributed environment. It has a microservices architecture, and flow-based programming model. Current tools like this do not enable streaming of data directly between operators.
This analysis estimates the cost of selected oil and biomass supply shocks for producers and consumers in the light-duty vehicle fuel market under various supply chain configurations using a mathematical programing model, BioTrans. The supply chain configurations differ by whether they include selected flexibility levers: multi-feedstock biorefineries; advanced biomass logistics; and the ability to adjust ethanol content of low-ethanol fuel blends, from E10 to E15 or E05. The simulated scenarios explore market responses to supply shocks including substitution between gasoline and ethanol, substitution between different sources of ethanol supply, biorefinery capacity additions or idling, and price adjustments. Welfare effects for the various market participants represented in BioTrans are summarized into a net shock cost measure. As oil accounts for a larger fraction of fuel by volume, its supply shocks are costlier than biomass supply shocks. Corn availability and the high cost of adding biorefinery capacity limit increases in ethanol use during gasoline price spikes. During shocks that imply sudden decreases in the price of gasoline, the renewable fuel standard (RFS) biofuel blending mandate limits the extent to which flexibility can be exercised to reduce ethanol use. The selected flexibility levers are most useful in response to cellulosic biomass supply shocks.
School buses are an essential component of the transportation infrastructure, serving as a lifeline for students across the globe. However, the widespread use of diesel school buses has raised concerns about the health impact on millions of students exposed to harmful emissions daily. Recognizing this issue, school districts worldwide are urgently seeking cleaner energy alternatives. Electric school buses emerge as an environmentally friendly and sustainable option, fostering a healthier environment for both students and communities. However, school bus electrification faces the challenges of high upfront cost, cumbersome charging management, and constraints from power grids. To help school bus operators address those challenges, this study presents a data-driven analysis for school bus electrification. This study considered a real-world school bus system in Richmond, VA, and developed a mathematical programming model to analyze the system design, charging strategies, and charging load profiles for the electrification scenario. The study evaluated different charging strategies based on model outcomes, aiming to optimize efficiency and effectiveness. Ultimately, this research generated electric school bus charging demand profiles under various scenarios, shedding light on the feasibility and implications of transitioning to electric-powered school buses.
The Open MPI for Exascale (OMPI-X) project was one of two in the Exascale Computing Project (ECP) focused on advancing the MPI ecosystem. The OMPI-X team worked with other MPI Forum members to champion several important features for inclusion in the MPI 4.0, 4.1, and upcoming 5.0 MPI standard versions, in support of the needs of exascale applications and systems. The team also worked with the larger Open MPI community to bring implementations of these new features and other enhancements into Open MPI, one of the leading open-source implementations of the MPI interface. Here, this paper describes the motivation for the work of the OMPI-X project in the context of exascale computing needs, the nature of the resulting new capabilities in the MPI standard, and how they were implemented in the Open MPI library. Features include improved support for “MPI + X” programming models through partitioned communications and support for user-level threading, sessions, fault tolerance through the user-level fault mitigation (ULFM) and Reinit models, and other features. We also discuss enhancements to Open MPI providing improved performance and scalability for existing features, such as collective operations, one-sided operations, support for the Slingshot-11 interconnect of the initial exascale systems, and how the OMPI-X team worked to improve quality assurance for the Open MPI library, particularly on platforms of interest to the Department of Energy community.
The Exascale Computing Project (ECP) Software Technology and Co-Design teams addressed the growing complexities in high-performance computing (HPC) by developing scalable software libraries and tools that leverage exascale system capabilities. As we enter the exascale era, the need for reusable, optimized software solutions that can handle the unique challenges posed by these systems becomes increasingly important. The primary challenges the ECP teams faced were to create software libraries and tools that are performant on exascale architectures and portable and usable across diverse hardware platforms. Efforts addressed issues related to concurrent execution, memory management, and the integration of heterogeneous computing resources, such as GPUs from multiple vendors. The ECP’s strategy involved a structured development process encompassing the creation, optimization, and deployment of software in collaboration with industry, academia, and national laboratories. The project was organized into several technical areas: co-design of domain-specific suites with target applications, programming models and runtimes, development tools, mathematical libraries, data and visualization tools, and software ecosystem and delivery mechanisms. ECP has successfully developed a large portfolio of software libraries and tools that demonstrate significant improvements in performance and scalability on exascale systems. These products have been integrated into the Department of Energy’s computing facilities, supporting various scientific applications and ensuring robust performance across different hardware setups. ECP advancements in software development for exascale computing highlight the importance of a collaborative and adaptive approach to handling next-generation HPC systems complexities. The lessons learned emphasize the need for continuous engagement with end-users and vendors, and the importance of maintaining a balance between innovation and practical implementation. Future efforts will focus on ensuring scalability, keeping pace with rapid hardware advancements, and further enhancing the interoperability and usability of the software ecosystem. In conclusion, subsequent articles in this special issue provide in-depth discussions and case studies into specific library and tool efforts.
The advent of exascale supercomputers heralds a new era of scientific discovery, yet it introduces significant architectural challenges that must be overcome for MPI applications to fully exploit its potential. Among these challenges is the adoption of heterogeneous architectures, particularly the integration of GPUs to accelerate computation. Additionally, the complexity of multithreaded programming models has also become a critical factor in achieving performance at scale. The efficient utilization of hardware acceleration for communication, provided by modern NICs, is also essential for achieving low latency and high throughput communication in such complex systems. In response to these challenges, the MPICH library, a high-performance and widely used Message Passing Interface (MPI) implementation, has undergone significant enhancements. Here, this paper presents four major contributions that prepare MPICH for the exascale transition. First, we describe a lightweight communication stack that leverages the advanced features of modern NICs to maximize hardware acceleration. Second, our work showcases a highly scalable multithreaded communication model that addresses the complexities of concurrent environments. Third, we introduce GPU-aware communication capabilities that optimize data movement in GPU-integrated systems. Finally, we present a new datatype engine aimed at accelerating the use of MPI derived datatypes on GPUs. These improvements in the MPICH library not only address the immediate needs of exascale computing architectures but also set a foundation for exploiting future innovations in high-performance computing. By embracing these new designs and approaches, MPICH-derived libraries from HPE Cray and Intel were able to achieve real exascale performance on OLCF Frontier and ALCF Aurora respectively.
This paper proposes a machine-learning (ML)-aided cognitive approach for effective bandwidth reconfiguration in optically interconnected datacenter/high-performance computing (HPC) systems. The proposed approach relies on a Hyper-X-like architecture augmented with flexible-bandwidth photonic interconnections at large scales using a hierarchical intra/inter-POD photonic switching layout. We first formulate the problem of the connectivity graph and routing scheme optimization as a mixed-integer linear programming model. A two-phase heuristic algorithm and a joint optimization approach are devised to solve the problem with low time complexity. Then, we propose an ML-based end-to-end performance estimator design to assist the network control plane with intelligent decision making for bandwidth reconfiguration. Numerical simulations using traffic distribution profiles extracted from HPC applications traces as well as random traffic matrices verify the accuracy performance of the ML design estimator ( < <#comment/> 9 % <#comment/> error) and demonstrate up to 5 × <#comment/> throughput gain from the proposed approach compared with the baseline Hyper-X network using fixed all-to-all intra/inter-portable data center interconnects.
Cellulosic butanol is a very promising renewable fuel to consider for the future transportation market. However, the seasonal availability of the raw materials, high maintenance cost, and high logistical cost of the biomass energy supply chain are the main factors impeding the commercialization and large-scale-production of this energy source. Furthermore, research focusing on an environmental or green supply chain network design of cellulosic butanol has been insufficient. Here, this study focused on designing a green supply chain network for cellulosic butanol. A life cycle analysis was integrated into a multi-objective linear programming model to optimize the cellulosic butanol supply chain network. With the objectives of maximizing the economic profits and minimizing the greenhouse gas emissions, the proposed model can optimize the location and size of a bio-butanol production plant. The mathematical model was applied to a case study in the state of Missouri, and solved the tradeoff between the feedstock and market availabilities of sorghum stem bio-butanol.
In 2012-2013, IBACOS worked with a builder, Brookfield Homes in Denver, Colorado, to design and construct a Passive House certified model home. IBACOS used several modeling programs and calculation methods to complete the final design package along with Brookfield's architect KGA Studio. This design package included upgrades to the thermal enclosure, basement insulation, windows, and heating, ventilation, and air conditioning. Short-term performance testing in the Passive House was done during construction and after construction. Testing with a blower door indicated that whole-building air leakage to the outside was 324 CFM and 0.60 ACH50. The other two test homes had little short-term testing done post-construction by the local energy rater. IBACOS then monitored the energy consumption and whole-house comfort conditions of that occupied Passive House after one year of operation and compared the monitoring results to those for two other occupied test houses in the same area with similar square footage but slightly different floor plans. IBACOS also assisted the builder, Brookfield Homes, in researching design scenarios for Zero Energy Ready Home and ENERGY STAR acceptance levels. IBACOS also assisted Brookfield in conceptualizing product for Denver's Brighton Heights area. Brookfield was considering building to Zero Energy Ready Home standards in that location. IBACOS provided strategies that Brookfield may draw from in the event the builder chooses to pursue a Zero Energy Ready Home plan for that market.
In 2012-2013, IBACOS worked with a builder, Brookfield Homes in Denver, Colorado, to design and construct a Passive House certified model home. IBACOS used several modeling programs and calculation methods to complete the final design package along with Brookfield's architect KGA Studio. This design package included upgrades to the thermal enclosure, basement insulation, windows, and heating, ventilation, and air conditioning. Short-term performance testing in the Passive House was done during construction and after construction.
Research and development of KIVA-hpFE for turbulent reactive and multiphase flow particularly as related to engine modeling program has relevance to National energy security and climate change. Climate change is a source problem, and energy national security is consumption of petroleum products problem. Accurately predicting engine processes leads to, lower greenhouse gas (GHG) emission, where engines in the transportation sector currently account for 26% of the U.S. GHG emissions. Less dependence on petroleum products leads to greater energy security. By Environmental Protection Agency standards, some vehicles are now reaching 42 to the 50 mpg mark. These are conventional gasoline engines. Continued investment and research into new technical innovations, the potential exists to save more than 4 million barrels of oil per day or approximately $200 to $400 million per day. This would be a significant decrease in emission and use of petroleum and a very large economic stimulus too! It is estimated with further advancements in combustion, the current emissions can be reduced up to 40%. Enabling better understanding of fuel injection and fuel-air mixing, thermodynamic combustion losses, and combustion/emission formation processes enhances our ability to help solve both problems. To provide adequate capability for accurately simulating these processes, minimize time and labor for development of engine technology, are the goals of our KIVA development program.
The Contingency Contractor Optimization Tool – Prototype (CCOT-P) database is used to store input and output data for the linear program model described in [1]. The database allows queries to retrieve this data and updating and inserting new input data.
The Hawai‘i Natural Energy Institute (HNEI) is conducting research to assess the technical potential of using an electrolyzer-based hydrogen (H2) production and storage system as a grid demand response tool using battery data from a 200 MW grid to show the kind of response required. The hydrogen produced by the electrolyzer is used for transportation. A 65 kg/day hydrogen energy system (HES) consisting of a PEM electrolyzer, 35 bar buffer tank, 450 bar compressor, and associated chiller systems was purchased and installed at the Hawaii Natural Energy Laboratory Hawaii Authority (NELHA) to demonstrate long-term durability of the electrolyzer under cyclic operation required for frequency regulation on an island grid system. The excess hydrogen was stored for use by three fuel-cell buses to be operated at Hawai‘i Volcanoes National Park (HAVO) and by the County of Hawai‘i Mass Transit Agency (MTA). This paper describes the site selection and equipment commissioning, plus a comprehensive test plan that was developed to characterize the performance and durability of the electrolyzer under dynamic load conditions. The controls were modified for the operating envelope and dynamic limits of the electrolyzer. While the data showed these modifications significantly improved the system response time, it is not fast enough to match a BESS response time for grid frequency management. The electrolyzer can only be used for slower acting changes (1 to 0.5 Hz). A potential solution is to design an electrolyzer/BESS hybrid system and develop a modeling program to find the optimum mix of battery and electrolyzer to provide the maximum grid regulation services at minimum cost.
This brief report highlights the experience gained with MosaStore, an exploratory part of the X-Stack project “ExM: System support for extreme-scale, many-task applications”. The ExM project proposed to use concurrent workflows supported by the Swift language and runtime as an innovative programming model to exploit parallelism in exascale computers. MosaStore aims to support this endeavor by improving storage support for workflow-based applications, more precisely by exploring the gains that can be obtained from co-designing the storage system and the workflow runtime engine. MosaStore has been developed primarily at the University of British Columbia.
The Department of Energy (DOE) Office of Science (SC) facilities including accelerators, light sources and neutron sources and sensors that study, the environment, and the atmosphere, are producing streaming data that needs to be analyzed for next-generation scientific discoveries. There has been an explosion of new research and technologies for stream analytics arising from the academic and private sectors. However, there has been no corresponding effort in either documenting the critical research opportunities or building a community that can create and foster productive collaborations. The two-part workshop series, STREAM: Streaming Requirements, Experience, Applications and Middleware Workshop (STREAM2015 and STREAM2016), were conducted to bring the community together and identify gaps and future efforts needed by both NSF and DOE. This report describes the discussions, outcomes and conclusions from STREAM2016: Streaming Requirements, Experience, Applications and Middleware Workshop, the second of these workshops held on March 22-23, 2016 in Tysons, VA. STREAM2016 focused on the Department of Energy (DOE) applications, computational and experimental facilities, as well software systems. Thus, the role of “streaming and steering” as a critical mode of connecting the experimental and computing facilities was pervasive through the workshop. Given the overlap in interests and challenges with industry, the workshop had significant presence from several innovative companies and major contributors. The requirements that drive the proposed research directions, identified in this report, show an important opportunity for building competitive research and development program around streaming data. These findings and recommendations are consistent with vision outlined in NRC Frontiers of Data and National Strategic Computing Initiative (NCSI) [1, 2]. The discussions from the workshop are captured as topic areas covered in this report's sections. The report discusses four research directions driven by current and future application requirements reflecting the areas identified as important by STREAM2016. These include (i) Algorithms, (ii) Programming Models, Languages and Runtime Systems (iii) Human-in-the-loop and Steering in Scientific Workflow and (iv) Facilities.
Research and development of KIVA-hpFE for turbulent reactive and multiphase flow particularly as related to engine modeling program has relevance to National energy security and climate change. Climate change is a source problem, and energy national security is consumption of petroleum products problem. Accurately predicting engine processes leads to, lower greenhouse gas (GHG) emission, where engines in the transportation sector currently account for 26% of the U.S. GHG emissions. Less dependence on petroleum products leads to greater energy security. By Environmental Protection Agency standards, some vehicles are now reaching 42 to the 50 mpg mark. These are conventional gasoline engines. Continued investment and research into new technical innovations, the potential exists to save more than 4 million barrels of oil per day or approximately $200 to $400 million per day. This would be a significant decrease in emission and use of petroleum and a very large economic stimulus too! It is estimated with further advancements in combustion, the current emissions can be reduced up to 40%. Enabling better understanding of fuel injection and fuel-air mixing, thermodynamic combustion losses, and combustion/emission formation processes enhances our ability to help solve both problems. To provide adequate capability for accurately simulating these processes, minimize time and labor for development of engine technology, are the goals of our KIVA development program.
This milestone is a tri-lab deliverable supporting ongoing Co-Design efforts impacting applications in the Integrated Codes (IC) program element Advanced Technology Development and Mitigation (ATDM) program element. In FY14, the trilabs looked at porting proxy application to technologies of interest for ATS procurements. In FY15, a milestone was completed evaluating proxy applications in multiple programming models and in FY16, a milestone was completed focusing on the migration of lessons learned back into production code development. This year, the co-design milestone focuses on extracting the knowledge gained and/or code revisions back into production applications.
The Dynamic, Exascale Global Address Space programming environment (DEGAS) project will develop the next generation of programming models and runtime systems to meet the challenges of Exascale computing. The Berkeley part of the project concentrated on communication-optimal code generation to optimize speed and energy efficiency by reducing data movement. Our work developed communication lower bounds, and/or communication avoiding algorithms (that either meet the lower bound, or do much less communication than their conventional counterparts) for a variety of algorithms, including linear algebra, machine learning and genomics. The Berkeley part of the project concentrated on communication-optimal code generation to optimize speed and energy efficiency by reducing data movement. Our work developed communication lower bounds, and/or communication avoiding algorithms (that either meet the lower bound, or do much less communication than their conventional counterparts) for a variety of algorithms, including linear algebra, machine learning and genomics.