Employing Blockchain Technology for Decentralized Crowdsourced Data Access and Management

The HHL algorithm is a prominent quantum algorithm that offers exponential speedup over its classical counterparts for solving a system of linear equations. However, synthesizing and executing HHL circuits demand significant computational resources from both classical and quantum systems. In this paper, we benchmark the HHL algorithm using the optimized Quantum Phase Estimation (QPE) generation algorithm, LuGo [1], to enhance its scalability and efficiency. We leverage various highperformance computing (HPC) systems–the National Energy Research Scientific Computing Center's (NERSC's) Perlmutter supercomputer and Oak Ridge Leadership Computing Facility's (OLCF's) Andes system and Frontier supercomputer–to evaluate the scalability of generating HHL circuits and to measure the time to simulate the generated circuits. Additionally, enabled by the Quantum Computing User Program (QCUP) of OLCF, we provide a comprehensive analysis of the algorithm's performance on various state-of-the-art superconducting and trappedion quantum devices, including studies on qubit connectivity, fidelity comparisons, and hardware compatibility and robustness. Our results offer preliminary insights into potential practical applications of the HHL algorithm enabled by LuGo and the performance of various types of quantum hardware. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>Notice: This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory, operated under Contract No. DE-AC02-05CH11231.

The next generation of symmetric multiprocessors will add another level of memory hierarchy and new barriers to effective use of supercomputers. Helping scientists overcome such barriers to using high-performance computers was a key objective in the relocation of the National Energy Research Scientific Computing Center (NERSC) to Ernest Orlando Lawrence Berkeley National Laboratory. The relocation makes it easier for NERSC to collaborate closely with scientists at Berkeley Lab and throughout the energy research community, with scientists at the adjacent University of California's Berkeley campus, and with major computer and data communications companies throughout the San Francisco Bay area.

IThe National Energy Research Scientific Computing Center (NERSC) is the primary computing center for the DOE Office of Science, serving approximately 4,000 users and hosting some 550 projects that involve nearly 700 codes for a wide variety of scientific disciplines. In addition to large-scale computing resources NERSC provides critical staff support and expertise to help scientists make the most efficient use of these resources to advance the scientific mission of the Office of Science. In May 2011, NERSC, DOE’s Office of Advanced Scientific Computing Research (ASCR) and DOE’s Office of Nuclear Physics (NP) held a workshop to characterize HPC requirements for NP research over the next three to five years. The effort is part of NERSC’s continuing involvement in anticipating future user needs and deploying necessary resources to meet these demands. The workshop revealed several key requirements, in addition to achieving its goal of characterizing NP computing. The key requirements include: 1. Larger allocations of computational resources at NERSC; 2. Visualization and analytics support; and 3. Support at NERSC for the unique needs of experimental nuclear physicists. This report expands upon these key points and adds others. The results are based upon representative samples, called “case studies,” of the needs of science teams within NP. The case studies were prepared by NP workshop participants and contain a summary of science goals, methods of solution, current and future computing requirements, and special software and support needs. Participants were also asked to describe their strategy for computing in the highly parallel, “multi-core” environment that is expected to dominate HPC architectures over the next few years. The report also includes a section with NERSC responses to the workshop findings. NERSC has many initiatives already underway that address key workshop findings and all of the action items are aligned with NERSC strategic plans.

Dark energy remains one of the least understood areas of modern cosmology and physics. The Dark Energy Spectroscopic Instrument (DESI) was built to improve our understanding through the acquisition of data from over thirty million galaxies. DESI, located at the four meter Mayall Telescope on Kitt Peak in Arizona, is a highly multiplexed instrument capable of acquiring astronomical spectra for 5000 objects simultaneously. This multiplexing, which is larger than any multi-object spectrograph to date, is made more ambitious with an observing strategy that requires up-to-date information of data quality and completeness on a daily basis to inform the scheduling of future observations. With both an order of magnitude larger data volume than its predecessors and more stringent survey demands, novel methods have been developed to rapidly process the data in near-real-time and make inferences based on the results. The data acquired from thirty highly sensitive cameras are transferred from local storage on the mountaintop to the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory. Using an urgent queue operating at NERSC, a parallelized set of routines process the images and extract astronomically relevant information within minutes to hours of acquisition, enabling daytime analysis by the operations manager to make observing program decisions for the following night. The implementation has been run at scale and tested in the spring of 2020 during commissioning of the instrument and proven successful at giving critical feedback to the observing team about the previous night's data quality. Quantitative and qualitative results will be discussed, along with planned improvements to the workflow before the survey begins full operation in 2021.

The National Energy Research Scientific Computing Center (NERSC) is the leading scientific computing facility supporting research within the Department of Energy's Office of Science. NERSC provides high-performance computing (HPC) resources to approximately 4,000 researchers working on about 400 projects. In addition to hosting large-scale computing facilities, NERSC provides the support and expertise scientists need to effectively and efficiently use HPC systems. In February 2010, NERSC, DOE's Office of Advanced Scientific Computing Research (ASCR) and DOE's Office of Basic Energy Sciences (BES) held a workshop to characterize HPC requirements for BES research through 2013. The workshop was part of NERSC's legacy of anticipating users future needs and deploying the necessary resources to meet these demands. Workshop participants reached a consensus on several key findings, in addition to achieving the workshop's goal of collecting and characterizing computing requirements. The key requirements for scientists conducting research in BES are: (1) Larger allocations of computational resources; (2) Continued support for standard application software packages; (3) Adequate job turnaround time and throughput; and (4) Guidance and support for using future computer architectures. This report expands upon these key points and presents others. Several 'case studies' are included as significant representative samples of the needs of science teams within BES. Research teams scientific goals, computational methods of solution, current and 2013 computing requirements, and special software and support needs are summarized in these case studies. Also included are researchers strategies for computing in the highly parallel, 'multi-core' environment that is expected to dominate HPC architectures over the next few years. NERSC has strategic plans and initiatives already underway that address key workshop findings. This report includes a brief summary of those relevant to issues raised by researchers at the workshop.

As high-performance computing (HPC) resources continue to grow in size and complexity, so too does the volume and velocity of the operational data that is associated with them. At such scales, new mechanisms and technologies are required to continuously gather, store, and analyze this data in near-real time from heterogeneous and distributed sources without impacting the underlying data center operations or HPC resource utilization. In this paper, we describe our experiences in designing and implementing an infrastructure for extreme-scale operational data collection, known as the Operations Monitoring and Notification Infrastructure (OMNI) at the National Energy Research Scientific Computing (NERSC) center at Lawrence Berkeley National Laboratory. OMNI currently holds over 522 billion records of online operational data (totaling over 125TB) and can ingest new data points at an average rate of 25,000 data points per second. Using OMNI as a central repository, facilities and environmental data can be seamlessly integrated and correlated with machine metrics, job scheduler information, network errors, and more, providing a holistic view of data center operations. To demonstrate the value of real-time operational data collection, we present a number of real-world case studies for which having OMNI data readily available led to key operational insights at NERSC. The case results include a reduction in the downtime of an HPC system during a facility transition, as well as a $2.5 million electrical substation savings for the next-generation Perlmutter HPC system.

The Solenoidal Tracker at RHIC (STAR) is a multi-national supported experiment located at Brookhaven National Lab. The raw physics data captured from the detector is on the order of tens of PBytes per data acquisition campaign, which makes STAR fit well within the definition of a big data science experiment. The production of the data has typically run on standard nodes or on standard Grid computing environments. All embedding simulations (complex workflow mixing real and simulated events) have been run on standard Linux resources at the National Energy Research Scientific Computing Center (NERSC) aka PDSF. However, HPC resources such as Cori have become available for STAR’s data production as well as embedding, and STAR has been the very first experiment to show feasibility of running a sustainable data production campaign on this computing resource. The use of Docker containers with Shifter is required to run on HPC @ NERSC – this approach encapsulates the environment in which a standard STAR workflow runs. From the deployment of a tailored Scientific Linux environment (requiring many of its own libraries and special configurations required to run) to the deployment of third-party software and the STAR specific software stack, it has become impractical to rely on a set of containers containing each specific software release. To this extent, solutions based on the CERN VM File System (CVMFS) for the deployment of software and services have been employed in HENP, but one needs to make careful scalability considerations when using a resource like Cori, such as not allowing all software to be deployed in containers or bare node. Additionally, CVMFS clients are not compatible on Cori nodes and one needs to rely on an indirect NFS/DVS mount scheme. In our contribution, we will discuss our strategies from the past and our current solution based on CVMFS. Furthermore, running on HPC is not a simple task as each aspect of the workflow must be enabled to scale, run efficiently, and the workflow needs to fit within the boundaries of the provided queue system (SLURM in this case). Lastly, we will also discuss what we have learned so far about what is the best method for grouping jobs to maximize a single 48 core HPC node within a specific time frame and maximize our workflow efficiency.

MapReduce is increasingly becoming a popular framework, and a potent programming model. The most popular open source implementation of MapReduce, Hadoop, is based on the Hadoop Distributed File System (HDFS). However, as HDFS is not POSIX compliant, it cannot be fully leveraged by applications running on a majority of existing HPC environments such as Teragrid and NERSC. These HPC environments typically support globally shared file systems such as NFS and GPFS. On such resourceful HPC infrastructures, the use of Hadoop not only creates compatibility issues, but also affects overall performance due to the added overhead of the HDFS. This paper not only presents a MapReduce implementation directly suitable for HPC environments, but also exposes the design choices for better performance gains in those settings. By leveraging inherent distributed file systems' functions, and abstracting them away from its MapReduce framework, MARIANE (MApReduce Implementation Adapted for HPC Environments) not only allows for the use of the model in an expanding number of HPC environments, but also allows for better performance in such settings. This paper shows the applicability and high performance of the MapReduce paradigm through MARIANE, an implementation designed for clustered and shared-disk file systems and as such not dedicated to a specific MapReduce solution. The paper identifies the components and trade-offs necessary for this model, and quantifies the performance gains exhibited by our approach in distributed environments over Apache Hadoop in a data intensive setting, on the Magellan test bed at the National Energy Research Scientific Computing Center (NERSC).

The complex software and hardware I/O stack of HPC platforms makes it challenging for end-users to obtain superior I/O performance and to understand the root causes of I/O bottlenecks they encounter. Despite the continuous efforts from the community to profile I/O performance and propose new optimization techniques and tuning options for improving the performance, there is still a translation gap between profiling and tuning. In this paper, we propose Drishti, a solution to guide scientists in optimizing I/O in their applications by detecting typical I/O performance pitfalls and providing recommendations. We illustrate its applicability in two case studies and evaluate its robustness and performance by summarizing the issues detected in over a hundred thousand Darshan logs collected on the Cori supercomputer at the National Energy Research Scientific Computing Center (NERSC). Drishti can empower end-users and guide them in the I/O optimization journey by shedding some light on everyday I/O performance pitfalls and how to fix them.

The volume and complexity of data produced and analyzed in scientific collaborations is growing exponentially. It is important to track scientific data-intensive analysis workflows to provide context and reproducibility as data is transformed in these collaborations. Provenance addresses this need and aids scientists by providing the lineage or history of how data is generated, used and modified. Provenance has traditionally been collected at the workflow level often making it hard to capture relevant information about resource characteristics and is difficult for users to easily incorporate in existing workflows. In this paper, we describe Milieu, a framework focused on the collection of provenance for scientific experiments in High Performance Computing systems. Our approach collects provenance in a minimally intrusive way without significantly impacting the performance of the execution of scientific workflows. We also provide fidelity to our provenance collection by allowing users to specify three levels of provenance collection. We evaluate our framework on systems at the National Energy Research Scientific Computing Center (NERSC) and show that the overhead is less than the variation already experienced by these applications in these shared environments.

DIII-D research is being accelerated by leveraging high performance computing (HPC) and data resources available through the National Energy Research Scientific Computing Center (NERSC) Superfacility initiative. As part of this initiative, a high-resolution, fully automated, whole discharge kinetic equilibrium reconstruction workflow was developed that runs at the NERSC for most DIII-D shots in under 20 min. This has eliminated a long-standing research barrier and opened the door to more sophisticated analyses, including plasma transport and stability. These capabilities would benefit from being automated and executed within the larger Department of Energy Advanced Scientific Computing Research program’s Integrated Research Infrastructure (IRI) framework. The goal of IRI is to empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation. For transport, we are looking at producing flux matched profiles and also using particle tracing to predict fast ion heat deposition from neutral beam injection before a shot takes place. Our starting point for evaluating plasma stability focuses on the pedestal limits that must be navigated to achieve better confinement. This information is meant to help operators run more effective experiments, so it needs to be available rapidly inside the DIII-D control room. So far this has been achieved by ensuring the data is available with existing tools, but as more novel results are produced new visualization tools must be developed. In addition, all of the high-quality data we have generated has been collected into databases that can unlock even deeper insights. This has already been leveraged for model and code validation studies as well as for developing AI/ML surrogates. The workflows developed for this project are intended to serve as prototypes that can be replicated on other experiments and can be run to provide timely and essential information for ITER, as well as next stage fusion power plants.

Some scientific and engineering applications need to compute a large number of eigenpairs of a large Hermitian matrix. Though the Lanczos method is effective for computing a few eigenvalues, it can be expensive for computing a large number of eigenpairs (e.g., in terms of computation and communication). To improve the performance of the method, in this paper, we study an s-step variant of thick-restart Lanczos (TRLan) combined with an explicit external deflation (EED). The s-step method generates a set of s basis vectors at a time and reduces the communication costs of generating the basis vectors. We then design a specialized matrix powers kernel (MPK) that further reduces the communication and computational costs by taking advantage of the special properties of the deflation matrix. We conducted numerical experiments of the new TRLan eigensolver using synthetic matrices and matrices from electronic structure calculations. The performance results on the Cori supercomputer at the National Energy Research Scientific Computing Center (NERSC) demonstrate the potential of the specialized MPK to significantly reduce the execution time of the TRLan eigensolver. The speedups of up to 3.1× and 5.3× were obtained in our sequential and parallel runs, respectively.

The Superfacility API brings automation to the use of High Performance Computing (HPC) systems. Our aim is to enable scientists to reliably automate their interactions with computational resources at the National Energy Research Scientific Computing Center (NERSC), removing human intervention from the process of transferring, analyzing, and managing data. In this paper, we describe the science use cases that drive the API design, our schema of API endpoints, and implementation details and considerations, including authentication and authorization. We also discuss future plans, working toward our vision of supporting entirely automated experiment-network-HPC workflows.

Widely used benchmarks, such as High Performance Linpack (HPL), do not always provide direct insights are notoriously poor indicators of into the actual application performance of systems. When real applications are used, and there have been are criticisms indicating that the performance of simplified benchmarks such as HPL no longer strongly correlate to real application performance. In contrast, performance evaluations based on real or mini applications may give a direct estimation into application performance. The Sustained System Performance (SSP) metric, which is used to evaluate systems based on the performance at scale of various applications, has been successfully adopted to procure systems at the National Energy Research Scientific Computing Center (NERSC), the National Center for Supercomputing Applications (NCSA) and other facilities. However, significant effort is required to tune and optimize several mini applications for each of systems. In this paper, we propose a new performance metric — the Simplified Sustained System Performance (SSSP) metric — based on a suite of simple benchmarks, which enables performance projection that correlates with full applications, but use onto a suite of mini applications. While the SSP metric is calculated over a set of applications, the SSSP metric applies its methodology to a set of benchmarks. Preliminary weighting factors for benchmarks are introduced to approximate the original SSP metric more accurately by the SSSP metric. To define the weighting factors, we perform a simple learning algorithm. Our preliminary experiments show that even though our metric is still easy to measure because it is based on a combination of simple benchmarks, it can provide projections of the performance of applications.

Storage system performance modeling is crucial for efficient use of heterogeneous shared resources on leadership class computers. Variability in application performance, particularly variability arising from concurrent applications sharing I/O resources, is a major hurdle in the development of accurate performance models. We adopt a deep learning approach based on conditional variational auto encoders (CVAE) for I/O performance modeling, and use it to quantify performance variability. We illustrate our approach using the data collected on Edison, a production supercomputing system at the National Energy Research Scientific Computing Center (NERSC). The CVAE approach is investigated by comparing it to a previously proposed sensitivity-based Gaussian process (GP) model. We find that the CVAE model performs slightly better than the GP model in cases where training and testing data come from different applications, since CVAE can inherently leverage the whole data from multiple applications whereas GP partitions the data and builds separate models for each partition. Hence, the CVAE offers an alternative modeling approach that does not need preprocessing; it has enough flexibility to handle data from a wide variety of applications without changing the inference approach.