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This presentation covers opportunities and challenges for accelerating nuclear deployment, specific application areas for artificial intelligence (AI) in nuclear power plant design, licensing, and deployment, some early R&D that has been conducted in this space at INL, and a look to the future.
Distributed wind listening session powerpoint presentation material, outlining to a general audience familiar with distributed wind why resiliency matters to distributed design and how the INL-developed ResDEEDs tool can be used to aid in resilient design.
The OpenMP language features have been evolving to meet the rapid development in hardware platforms. DOE applications tend to push the bleeding edge of features ratified in the OpenMP specification and tend to expose the rough edges of the features' implementations. The software harness on DOE supercomputers such as Titan and (upcoming) Summit include Cray, Clang, Flang, XL and GCC compilers. It is critical, especially for Summit, that the compilers support OpenMP offloading features. This paper focuses on evaluating support for OpenMP 4.5 target offload directives across compiler implementations on Titan and Summitdev, an early access system, which is one generation removed from Summit's architecture enabling application teams to test the systems' architecture. Our tests not only evaluate the OpenMP implementations but also expose ambiguities in the OpenMP 4.5 specification. We also evaluate compiler implementations using kernels extracted from production DOE applications. This helps in assessing the interaction of different OpenMP directives independent of other application artifacts. We are aware that the implementations are constantly evolving and are advertised as having only partial OpenMP 4.x support. We see this as a synergistic effort to help identify and correct features that are required by DOE applications and prevent deployment delays later on. Going forward, we also plan to interact with standard benchmarking bodies like SPEC/HPG to donate our tests and mini-apps/kernels for potential inclusion in the next release versions of SPEC OMP and SPEC ACCEL benchmark suites.
Oak Ridge National Laboratory's (ORNL's) Leadership Computing Facility (OLCF) continues to surpass its operational target goals: supporting users; delivering fast, reliable computational ecosystems; creating innovative solutions for high performance computing (HPC) needs; and managing risks, safety, and security associated with operating some of the most powerful computers in the world. The results can be seen in the cutting-edge science conducted by users and the praise from the research community. Calendar year (CY) 2019 was a big year as OLCF staff ran five world-class resources (the leadershipclass computers Titan and Summit, the large analysis cluster called Eos, and the massive parallel filesystems called Atlas and Alpine)) and also began power and cooling upgrades for a 2021 exascale system called Frontier. While continuing exceptional operation of Titan, Eos, and Rhea, the OLCF released the Summit supercomputer for production on January 1, 2019. Summit debuted as the most capable and efficient system in its class and has been recognized as the most powerful system in the world for its performance on both the high performance linpack (HPL) and conjugate gradient (HPCG) benchmark applications since June 2018 according to TOP500. Summit represents the culmination of a multiyear effort between the OLCF, IBM, NVIDIA, and Mellanox to deliver a system that is unmatched for modeling, simulation, data analysis, and learning. To hit the ground running with science-ready applications on day one, application teams worked closely with the OLCF through the Center for Accelerated Application Readiness (CAAR) program for years in advance of the Summit deployment. CY 2019 was filled with outstanding results and accomplishments: a very high rating from users on overall satisfaction for the sixth year in a row; a tremendous amount of core-hours delivered to researchers from two leadership-class systems; and success in delivering on the allocation split of roughly 60%, 30%, and 10% of core-hours offered for the Innovative and Novel Computational Impact on Theory and Experiment (INCITE), Advanced Scientific Computing Research Leadership Computing Challenge (ALCC), and Director's Discretionary (DD) programs, respectively (see Operational Performance section). These accomplishments, coupled with the high utilization rates (overall and capability usage), represent the fulfillment of the promise of both leadership-class machines: efficient facilitation of leadership-class computational applications. Table ES.1 presents a summary of the 2019 OLCF metric targets and the associated results. More information can be found in the Operational Performance section for each OLCF resource. The scientific accomplishments of OLCF users are a strong indication of long-term operational success, with publications this year in such notable journals and publications as Nature, Nature Physics, Nature Plants, Physical Review X, Journal of the American Physical Society, Cell, Nano Letters, and Trends in Biotechnology. Crucial domain-specific discoveries facilitated by resources at the OLCF are described in the High Performance Computing Facility Operational Assessment 2019 Oak Ridge Leadership Computing Facility (OAR) Strategic Results section. For example, researchers used Summit to pinpoint and understand the production of proteins from genetic information, including mutations and the functional expression of disease (Section 8.2).
Execution of heterogeneous workflows on high-performance computing (HPC) platforms present unprecedented resource management and execution coordination challenges for runtime systems. Task heterogeneity increases the complexity of resource and execution management, limiting the scalability and efficiency of workflow execution. Re-source partitioning and distribution of tasks execution over portioned re-sources promises to address those problems but we lack an experimental evaluation of its performance at scale. Here this paper provides a performance evaluation of the Process Management Interface for Exascale (PMIx) and its reference implementation PRRTE on the leadership-class HPC plat-form Summit, when integrated into a pilot-based runtime system called RADICAL-Pilot. We partition resources across multiple PRRTE Distributed Virtual Machine (DVM) environments, responsible for launching tasks via the PMIx interface. We experimentally measure the work-load execution performance in terms of task scheduling/launching rate and distribution of DVM task placement times, DVM startup and termination overheads on the Summit leadership-class HPC platform. Integrated solution with PMIx/PRRTE enables using an abstracted, standardized set of interfaces for orchestrating the launch process, dynamic process management and monitoring capabilities. It extends scaling capabilities allowing to overcome a limitation of other launching mechanisms (e.g., JSM/LSF). Explored different DVM setup configurations provide insights on DVM performance and a layout to leverage it. Our experimental results show that heterogeneous workload of 65,500 tasks on 2048 nodes, and partitioned across 32 DVMs, runs steady with resource utilization not lower than 52%. While having less concurrently executed tasks resource utilization is able to reach up to 85%, based on results of heterogeneous workload of 8200 tasks on 256 nodes and 2 DVMs.
In this work, we present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.
Oak Ridge National Laboratory’s (ORNL’s) Leadership Computing Facility (OLCF) continues to surpass its operational target goals of supporting users; delivering fast, reliable computational ecosystems; creating innovative solutions for high-performance computing (HPC) needs; contributing to the community to build the next generation HPC workforce, and managing risks, safety, and security associated with operating some of the most powerful computers in the world. The results can be seen in the cutting-edge science conducted by users and the praise from the research community. Calendar year (CY) 2021 saw continued excellence in research supported by the OLCF’s leadership-class computing resources, including Summit (the nation’s most powerful supercomputer), the global scratch file system Alpine, the Scalable Protected Infrastructure (SPI), the Exploratory Visualization Environment for Research in Science and Technology (EVEREST), and the archival mass-storage resource High-Performance Storage System (HPSS). While maintaining access and exceptional user support for Summit, the OLCF continued to make progress on the installation and deployment of Frontier, which will be the nation’s first exascale system when it comes online at the start of CY 2023. Users have already begun running and optimizing scientific codes on Crusher, the OLCF test and development system equipped with Frontier’s architecture. Throughout the year, the OLCF maintained a strong culture of operational excellence, including risk management, workplace safety, and cybersecurity. The OLCF’s rigorous risk management strategy anticipated and mitigated risks, and at this time there are no high-priority operational risks. Similarly, ORNL and the OLCF were committed to operating under the US Department of Energy’s (DOE’s) safety regulations that ensure a safe workplace. Technical staff tracked and monitored existing threats and vulnerabilities within the OLCF while continually developing tools and practices to enhance operations without increasing the facility’s risk. CY 2021 was filled with outstanding results and accomplishments, including a very high rating from users on overall satisfaction for the eighth consecutive year; a tremendous number of node hours delivered to 1,671 researchers on Summit; and the successful delivery of the allocation split of roughly 60%, 20%, and 20% of core-hours offered for the Innovative and Novel Computational Impact on Theory and Experiment (INCITE), Advanced Scientific Computing Research Leadership Computing Challenge (ALCC), and Director’s Discretionary (DD) programs, respectively (Section 2). COVID-19 research remained a focus in 2021, and the ALCC and DD programs allocated over 1 million Summit hours to the COVID-19 High Performance Computing Consortium. These accomplishments, coupled with the high utilization rates (i.e., overall and capability usage), represent the fulfillment of the promise of leadership class machines: efficient facilitation of leadership-class computational applications.
Improving the understanding of how computational fluid dynamics (CFD) direct numerical simulations (DNS) of flows in the High Flux Isotope Reactor (HFIR) perform when run in parallel using the high performance computing (HPC) platform Summit at the Oak Ridge Leadership Computing Facility (OLCF) is of particular importance to boost the computational tools used to support HFIR conversion to low enriched fuel (LEU). Evaluation of scaling performance was driven by the increasing importance of graphics processing unit (GPU) usage in HPC, which is becoming the standard for modern supercomputers such as Summit. The desired results are to obtain a strong positive correlation between the computational resources dedicated to a problem and the relative speed-up of the simulation in comparison to a benchmark. This capability will allow substantially improvement in HFIR flow analytical capabilities, specifically when predicting turbulence properties at high Reynolds numbers. The study leverages previous simulation results performed with code PHASTA (finite element) on HPC platforms Cori (NERSC) and Theta (ALCF) [1] with computing options provided in the computing platform OpenFOAM (finite volume) at OLCF. Transitioning from PHASTA to OpenFOAM will (1) eliminate dependence on third-party software for mesh generation and manipulation, (2) reduce resource needs by employing modern architectures, and (3) build expertise for future modeling of HFIR-specific problems like heat transfer in involute geometry, entrance effects, flow structure in channel corners, and so on—all important issues when defining the available thermal margins in the transition to LEU. CPUs and GPUs differ significantly in their architecture and utilization, as discussed in the literature [2]. The most important differences are in the approach to computations and their memory. A single GPU contains a large quantity of cores, enabling it to perform with a much higher throughput than a CPU, but execution requires a different approach. GPU codes execute instructions using the Single-Instruction Multiple-Thread (SIMT) approach in which a single instruction is used for groups of threads called warps. A warp typically consists of 32 threads which must execute the same set of instructions, although on separate threads. Alternately, a CPU has far fewer cores that are much more flexible in their operation, excelling at quickly performing more complex serial computations. This is why GPUs have greater throughput when properly utilized. The second important difference is seen when comparing their memory spaces. Limited memory allocations and CPU–GPU communications cause a significant bottleneck in GPU-accelerated programs. Further study was required to properly take advantage of GPU resources. A comprehensive analysis of code performance and the model-specific features of turbulence constitutes the core of this work. In this study, a DNS simulation of HFIR channel turbulence was performed with the finite volume CFD code OpenFOAM v2112 and CUDA v11.0 on Red Hat Enterprise Linux v8.2. The OpenFOAM installation had AMGx integrated to enable GPU acceleration and utilizes the PETSc4FOAM library. The computational resources and the problem size were scaled on CPU and CPU + GPU architectures to gain a better understanding of the performance of a DNS problem on modern computing hardware. The study aimed to analyze the scaling of the code exclusively on CPUs and then to examine the scaling of the codes with GPU acceleration enabled. Scaling studies included CPU and GPU acceleration on a mesh of varying resolution to analyze the impact of problem size relative to computational resources. In the course of preparing the GPU configuration on Summit, mainly using the AMGX solvers, difficulties were encountered stemming from constant changes resulting from extensive ongoing development activities and the changing environment. This resulted in the inability to complete the GPU portion of the work. The code was compiled and tested, but production runs to assess acceleration were not performed because the used discretional compute time allocation expired as year-end approached. The Summit HPC platform is scheduled for decommissioning in 2024, making it unattractive for future use with Nvidia-based GPUs. Therefore, the work will be moved onto NERSC machines in FY24. An application was prepared and submitted, and sufficient node-hours were awarded to continue the research in the next calendar year. This report summarizes work performed thus far, which mostly focused on CPU OpenFOAM computing.
Most Alaskan volcanoes are glacier covered and provide excellent opportunities to study interactions between glaciers and volcanoes. The present paper is concerned with such a study, taking into account the Mt. Wrangell (4317 m) which is the northernmost active volcano (solfatara activity) on the Pacific Rim (62 deg N; 144 deg W). While the first photographs on the summit of Mt. Wrangell were published more than 75 years ago, research there began in 1953 and 1954. Satellite images reveal activity at the summit of Mt. Wrangell. However, the resolution is not sufficient for conducting important measurements regarding ice volume losses. For this reason, vertical aerial photographs of the summit were obtained, and a field trip to the summit was conducted. Aspects of photogrammetry are discussed, taking into account questions of ground control, aerial photography, topographic mapping, digital cross sections, and orthophotos.
Over the past 5 years, we have conducted field studies on the summit cones of Mauna Kea Volcano in Hawaii. Basaltic materials on several of these summit cones have been significantly altered by hydrothermal activity, whereas other summit cones have not undergone extensive aqueous alteration. These summit cones provide an important environment to study analogs of Martian surface alteration materials and analog surface processes (i.e., hydrothermal alteration, palagonitization. In on-going research, we have characterized the thermal and evolved gas behaviors of volatile-bearing reference minerals at reduced pressures in support of TEGA. We have expanded those studies to characterize the thermal and evolved gas behaviors of Mauna Kea samples as an analog for Mars surface materials. These samples were chosen for two reasons. First, many have chemical and spectral properties similar to what we see from remote sensing data from the Martian surface. Second, these samples have a range of mineralogical and chemical properties, ranging from simple to very complex; hence it will be challenging to characterize their thermal and evolved gas behaviors and to provide a context for their geological evolution. However, these types of studies are absolutely critical to understanding relevant geologic processes on Mars during robotic missions to the surface.
Summary The Oak Ridge Leadership Computing Facility (OLCF) has a long history of supporting and promoting GPU‐accelerated computing starting with the deployment of the Titan supercomputer in 2021 and continuing with the Summit supercomputer which has a theoretical peak performance of approximately 200 petaflops. Because the majority of Summit's computational power comes from its 27,972 GPUs, users must port their applications to one of the supported programming models in order to make efficient use of the system. To prepare the transition to Frontier, the OLCF's exascale supercomputer, users will need to adapt to an entirely new ecosystem which will include new hardware and software technologies. First, users will need to familiarize themselves with the AMD Radeon GPU architecture. Furthermore, users who have been previously relying on CUDA will need to transition to the Heterogeneous‐Computing Interface for Portability (HIP) or one of the other supported programming models (e.g., OpenMP, OpenACC). In this work, we describe our initial experiences and lessons learned in porting three applications or proxy apps currently running on Summit to the HPE/Cray ecosystem to leverage the compute power from AMD GPUs: minisweep, GenASiS, and Sparkler. Each one is representative of current production workloads utilized at the OLCF, different programming languages, and different programming models.
Designing and refactoring complex scientific code, such as the E3SM land model (ELM), for new computing architectures is challenging. This paper presents design strategies and technical approaches to develop a data-oriented, GPU-ready ELM model using compiler directives (OpenACC/OpenMP). We first analyze the datatypes and processes in the original ELM code. Then we present design considerations for ultrahigh-resolution ELM (uELM) development for massive GPU systems. These techniques include the global data-oriented simulation workflow, domain partition, code porting and data copy, memory reduction, parallel loop restructure and flattening, and race condition detection. We implemented the first version of uELM using OpenACC targeting the NVidia GPUs in the Summit supercomputer at Oak Ridge National Laboratory. During the implementation, we developed a software tool (named SPEL) to facilitate code generation, verification, and performance tuning using these techniques. The first uELM implementation for Nvidia GPUs on Summit delivered promising results: 1) over 98% of the ELM code was automatically generated and tuned by scripts. Most ELM modules had better computational performances than the original ELM code for CPUs. The GPU-ready uELM is more scalable than the CPU code on fully-loaded Summit nodes. Example profiling results from several modules are also presented to illustrate the performance improvements and race condition detection. The lessons learned and toolkit developed in the study are also suitable for further uELM deployment using OpenMP on the first US exascale computer, Frontier, equipped with AMD CPUs and GPUs.
Here, in this paper, performance strategies on GPU-based HPC platforms of a cellular automata (CA) simulation code for non-equilibrium solidification, including nucleation, grain growth, solute partitioning and transport for the metal additive manufacturing (AM) process are investigated using OpenMP 4.5. To accurately report the speed-up for multicore CPUs and GPUs, a rigorous performance analysis employed optimizations appropriate for both CPU-only code (baseline) and GPU offload codes for an isothermal test problem. The performance results on Summit at the Oak Ridge Leadership Computing Facility indicate that using a precomputed list of interface cells significantly decreased the wall-clock time on GPUs. The speedup due to GPU acceleration was evaluated for a full Summit node and measured to be 1.8X when comparing a 6 MPI tasks run with 6 GPUs versus 36 MPI tasks on the CPU only. That speed-up was found to be 7.9X when comparing 6 MPI tasks with 6 GPUs versus the 6 MPI tasks running on the CPU only. Performance measurements showed that system total time is almost constant for runs with more than 96 MPI tasks (or GPUs), indicating that the GPU-accelerated code showed an excellent weak scaling performance. Finally, a rapid directional solidification problem was considered to demonstrate the CA code capability on Summit. It was found that a mesh size of at least 0.05 μm is recommended for the AM-like simulations in order to obtain accurate elongated grain microstructure and elongated subgrain features, which are in qualitative good agreement with experimental data. The results presented in this study indicate that the performance strategies on GPU-based HPC platforms for the CA code are appropriate for novel HPC exascale platforms.
Constraining glacial history and process on Mt Mansfield, the highest peak in Vermont (1339 m a.s.l.), provides insight into how the Laurentide Ice Sheet shaped the underlying landscape, when latest Pleistocene ice retreated, and how upland and lowland glacial histories relate. Here, we quantify in situ cosmogenic 10 Be in 20 bedrock and boulder surfaces, as well as in situ cosmogenic 14 C in three of those surfaces, to assess subglacial erosion and exposure history. Further, isotopic concentrations indicate that Mt. Mansfield's lower elevations (~400–1200 m a.s.l.) were deeply eroded by at least several meters during the last glaciation and then deglaciated rapidly; 10Be ages across this elevation span are indistinguishable and average 13.9 ± 0.6 ka (n = 15), suggesting that 800 m of ice thinning occurred within at most about a millennium. Conversely, the higher elevations (>1200 m a.s.l.) preserve a more complex geomorphic history. Mt. Mansfield's summit surfaces contain 10 Be from previous periods of exposure, indicating that the mountaintop landscapes were likely preserved beneath cold-based, weakly-erosive glacial ice. Exposure ages from the shorter-lived isotope, 14 C, are younger (9.7 and 11.7 ka), suggesting that Mt. Mansfield's summit was covered until the early Holocene, perhaps by snowfields, ice carapaces, and/or till. Our findings, in context of previous work, suggest that thinning Laurentide ice flowed through the valleys for at most hundreds of years following deglaciation of the uplands, but that the summit remained shielded by ice or sediment for millennia after the valleys became ice-free.
Abstract Petrological and geochemical (major element, trace element, Sr–Nd isotope) data for recent (<5 kyr old) basalts that sporadically erupt on the western flank of Piton de la Fournaise (PdF), one of the most active volcanoes on Earth, allow the tracking of magma transfer and evolution from mantle to crustal depths. In the western peripheral area of PdF we document the broadly synchronous eruptions of (1) primitive olivine and olivine–clinopyroxene transitional basalts with tholeiitic affinity that are closely associated in space with (2) transitional olivine basalts with alkaline affinity, and (3) hybrid lavas, intermediate between the ‘alkaline’ and the ‘tholeiitic’ end-members. The composition of the latter overlaps with that of the lavas frequently erupted from the conduit system feeding the main summit cone. AlphaMELTS modelling, and fluid inclusion and clinopyroxene barometry, constrain the conditions of magma storage at 10–30 km, and the ascent of magma from the upper mantle to the shallow crustal plumbing system. Variable degrees of mantle melting, together with minor source heterogeneity and contamination with cumulate-derived partial melts, contribute to the diversity of PdF magmas. However, all these processes do not represent the dominant factors that produce the large variability we found in major element composition. Indeed, the composition of basalts erupted from PdF peripheral centers is strongly controlled by polybaric olivine–clinopyroxene fractionation at pressures higher than 3 kbar. Crystal textures and geochemical modelling suggest that fast magma ascent is critical to prevent clinopyroxene dissolution. Conversely, long-lasting magma stagnation promotes pyroxene resorption and magma differentiation. ‘Central’ eruptions occurring close to the PdF summit cone emit variably more evolved melts, which result from olivine–clinopyroxene–plagioclase differentiation at intermediate–shallow pressure (<3 kbar and in most cases <1 kbar). Deep and extensive magma mixing before injection into the crustal magma conduit system, located below the summit region, results in the apparent homogeneity of basalts erupted from the central area. As regards ‘peripheral’ eruptions, deep-seated stagnation of basaltic melts and differentiation at the mantle–crust transition zone (c. 4 kbar) produces a range of magma compositions. We demonstrate that rapid magma ascent from deep-seated reservoirs can bypass the central plumbing system. The eruptions of these magmas both in the central area and on the densely populated flanks have major consequences in terms of volcanic hazard at PdF.
As we approach the exascale computing era, the focused understanding of power consumption and its overall constraint on HPC architectures and applications are becoming increasingly paramount. Summit, located at the Oak Ridge Leadership Computing Facility (OLCF), is one of the fastest and largest pre-exascale platforms in operation today. This paper provides a first-order examination and analysis of power consumption at the component-level, node-level, and system-level, from all 4,626 Summit compute nodes, each with over 100 metrics at 1Hz frequency over the entire year of 2020. We also investigate the power characteristics and energy efficiency of over 840k Summit jobs and 250k GPU failure logs for further operational insights. To the best of our knowledge, this is the first systematic analysis of power data of HPC system at this scale.
In recent years, bike share programs have become more popular as they contribute to the move toward sustainable mobility in cities. Electric bike sharing, however, remains in the early stages of development. In contrast to traditional bikes, electric bicycles (e-bikes) provide an extra boost via an electric pedal-assist motor, thereby making it much easier to travel around a city with a hilly terrain, such as Park City, Utah. The Summit Bike Share system in Park City is the nation’s first fully electric bike share system (e-BSS). Based on an analysis of historical trip data of Summit Bike Share, this paper presents the system’s performance experience and evaluates user characteristics and travel behavior. A Poisson regression analysis is performed to investigate the factors that influence the e-bike share usage of this e-BSS. The regression results reveal that weather factors, including temperature and wind speed, significantly affect e-bike share usage. It was also found that weekends, summer months, high population density, proximity to public transit centers, recreational centers, and bike trails positively affect the demand for e-bikes. The findings of this paper can help the operators of Summit Bike Share to better understand their users and their e-BSS, while also providing a guide for other e-bike projects currently in the planning stages.
This report presents a summary of the activities of the Advanced Research Projects Agency-Energy (ARPA-E) during Fiscal Year 2014 (FY2014). In FY2014, ARPA-E issued funding solicitations and selected projects for three new programs covering a broad array of energy technologies, including: 27 million dollars to develop next-generation power switching devices that improve energy efficiency in a wide range of applications; 30 million dollars for hybrid solar energy beyond current photovoltaic (PV) and concentrated solar power (CSP) technologies; and 33 million dollars for electrochemical technologies to enable low-cost distributed power generation. ARPA-E released three additional funding opportunities in FY2014 with project selections that were ultimately announced in FY2015. These programs include: 30 million dollars for localized heating and cooling devices to expand temperature ranges within buildings reducing energy consumption; 30 million dollars for low-cost, high-sensitivity systems to detect and measure methane emissions; and 30 million dollars for low-cost tools to aid in the future development of fusion power. ARPA-E also continued the use of a rolling open solicitation to quickly support innovative applied energy research that has the potential to lead to new focused programs. In addition to these new programs, ARPA-E hosted the fifth annual Energy Innovation Summit from February 24-26, 2014. The Summit brought together leaders from academia, government, and business to discuss the foremost energy issues, showcase the latest technology innovations, and cultivate relationships to help advance cutting-edge technologies to market. The event drew over 2,100 attendees and featured over 120 speakers and keynote addresses. At the Summit, ARPA-E announced at least 24 ARPA-E project teams that have formed new companies and more than 16 ARPA-E projects, which have partnered with other government agencies for further development. Additionally, 22 ARPA-E projects have attracted more than 625 million dollars in private-sector follow-on funding after ARPA-E’s investment of approximately 95 million dollars. Lastly, ARPA-E continued to focus on providing awardees with practical training and critical business information as part of the Agency’s Technology-to-Market program. This support equips projects with a clear understanding of market needs to guide technical development and help projects succeed the marketplace.