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DOE OSTI2021

Reinspection of a Clinical Proteomics Tumor Analysis Consortium (CPTAC) Dataset with Cloud Computing Reveals Abundant Post-Translational Modifications and Protein Sequence Variants

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has provided some of the most in-depth analyses of the phenotypes of human tumors ever constructed. Today, the majority of proteomic data analysis is still performed using software housed on desktop computers which limits the number of sequence variants and post-translational modifications that can be considered. The original CPTAC studies limited the search for PTMs to only samples that were chemically enriched for those modified peptides. Similarly, the only sequence variants considered were those with strong evidence at the exon or transcript level. In this multi-institutional collaborative reanalysis, we utilized unbiased protein databases containing millions of human sequence variants in conjunction with hundreds of common post-translational modifications. Using these tools, we identified tens of thousands of high-confidence PTMs and sequence variants. We identified 4132 phosphorylated peptides in nonenriched samples, 93% of which were confirmed in the samples which were chemically enriched for phosphopeptides. In addition, our results also cover 90% of the high-confidence variants reported by the original proteogenomics study, without the need for sample specific next-generation sequencing. Finally, we report fivefold more somatic and germline variants that have an independent evidence at the peptide level, including mutations in ERRB2 and BCAS1. In this reanalysis of CPTAC proteomic data with cloud computing, we present an openly available and searchable web resource of the highest-coverage proteomic profiling of human tumors described to date.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Computational Strategies for Scalable Genomics Analysis

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Exploring Cloud Computing for Large-scale Scientific Applications

This paper explores cloud computing for large-scale data-intensive scientific applications. Cloud computing is attractive because it provides hardware and software resources on-demand, which relieves the burden of acquiring and maintaining a huge amount of resources that may be used only once by a scientific application. However, unlike typical commercial applications that often just requires a moderate amount of ordinary resources, large-scale scientific applications often need to process enormous amount of data in the terabyte or even petabyte range and require special high performance hardware with low latency connections to complete computation in a reasonable amount of time. To address these challenges, we build an infrastructure that can dynamically select high performance computing hardware across institutions and dynamically adapt the computation to the selected resources to achieve high performance. We have also demonstrated the effectiveness of our infrastructure by building a system biology application and an uncertainty quantification application for carbon sequestration, which can efficiently utilize data and computation resources across several institutions.

Lin, Guang↗

DOE OSTI2022

NREL Stratus - Enabling Workflows to Fuse Data Streams, Modeling, Simulation, and Machine Learning

Integrating cloud services into advanced computing facilities provides significant new capabilities over focusing solely on traditional high performance computing (HPC) workloads. This brings complementary capabilities as well as enabling new focused roles for HPC. They are especially potent for workflows that fuse data streams, modeling and simulation ('modsim') and machine learning. A key challenge to adopting a hybrid edge-cloud-HPC model is to align optimal capability, data, and user intent on the right resources for each step in a workflow.?The NREL Stratus service provides a basis for this: Stratus layers capabilities needed to make?cloud services accessible to a lab-based scientific community on commercial offerings, and; currently supports upwards of 200 projects ranging from IOT integration to traditional modeling and simulation. This provides a real-world inventory of scientific workflow elements. A growing knowledge base enables placing these elements appropriately between the edge, cloud, and traditional HPC. This paper outlines a vision via reference architecture and the application of that architecture in a typical workflow highlighting multiple components: sensor data intake, cleaning and transforming (edge/cloud suitable); generation of synthetic data through modsim, computationally heavy ML training and hyperparameter optimization (HPC suitable), and; inference and deployment (cloud ideal). Every step in such a workflow involves a cost-benefit analysis regarding the data movement, computational efficiency, availability, latency, and resource capabilities. The reference architecture and examples outlined allow for understanding new opportunities in the context of emerging workflows that combine IOT, cloud, and HPC to bolster scientific productivity.

AI↗

DOE OSTI2022

28 NREL Stratus - Enabling Workflows to Fuse Data Streams, Modeling, Simulation, and Machine Learning: Preprint

AI↗

DOE OSTI2021

A cloud platform for atomic pair distribution function analysis: PDFitc

A cloud web platform for analysis and interpretation of atomic pair distribution function (PDF) data ( PDFitc ) is described. The platform is able to host applications for PDF analysis to help researchers study the local and nanoscale structure of nanostructured materials. The applications are designed to be powerful and easy to use and can, and will, be extended over time through community adoption and development. The currently available PDF analysis applications, structureMining, spacegroupMining and similarityMapping , are described. In the first and second the user uploads a single PDF and the application returns a list of best-fit candidate structures, and the most likely space group of the underlying structure, respectively. In the third, the user can upload a set of measured or calculated PDFs and the application returns a matrix of Pearson correlations, allowing assessment of the similarity between different data sets. structureMining is presented here as an example to show the easy-to-use workflow on PDFitc . In the future, as well as using the PDFitc applications for data analysis, it is hoped that the community will contribute their own codes and software to the platform.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2013

Performance implications from sizing a VM on multi-core systems: A Data analytic application s view

In this paper, we present a quantitative performance analysis of data analytics applications running on multi-core virtual machines. Such environments form the core of cloud computing. In addition, data analytics applications, such as Cassandra and Hadoop, are becoming increasingly popular on cloud computing platforms. This convergence necessitates a better understanding of the performance and cost implications of such hybrid systems. For example, the very rst step in hosting applications in virtualized environments, requires the user to con gure the number of virtual processors and the size of memory. To understand performance implications of this step, we benchmarked three Yahoo Cloud Serving Benchmark (YCSB) workloads in a virtualized multi-core environment. Our measurements indicate that the performance of Cassandra for YCSB workloads does not heavily depend on the processing capacity of a system, while the size of the data set is critical to performance relative to allocated memory. We also identi ed a strong relationship between the running time of workloads and various hardware events (last level cache loads, misses, and CPU migrations). From this analysis, we provide several suggestions to improve the performance of data analytics applications running on cloud computing environments.

Lim, Seung-Hwan↗

DOE OSTI2023

Spy in the GPU-box: Covert and Side Channel Attacks on Multi-GPU System

The deep learning revolution has been enabled in large part by GPUs, and more recently accelerators, which make it possible to carry out computationally demanding training and inference in acceptable times. As the size of machine learning networks and workloads continues to increase, multi-GPU machines have emerged as an important platform offered on High Performance Computing and cloud data centers. Since these machines are shared among multiple users, it becomes increasingly important to protect applications against potential attacks. In this paper, we explore the vulnerability of Nvidia's DGX multi-GPU machines to covert and side channel attacks. These machines consist of a number of discrete GPUs that are interconnected through a combination of custom interconnect (NVLink) and PCIe connections. We reverse engineer the interconnected cache hierarchy and show that it is possible for an attacker on one GPU to cause contention on the L2 cache of another GPU. We use this observation to first develop a covert channel attack across two GPUs, achieving the best bandwidth of around 4 MB/s. We also develop a prime and probe attack on a remote GPU allowing an attacker to recover the cache access pattern of another workload. This access pattern can be used in any number of side channel attacks: we demonstrate a proof of concept attack that fingerprints the application running on the remote GPU, with high accuracy. We also develop a proof of concept attack to extract hyperparameters of a machine learning workload. Our work establishes for the first time the vulnerability of these machines to microarchitectural attacks and can guide future research to improve their security.

Dutta, Sankha↗

DOE OSTI2015

Performance analysis of data intensive cloud systems based on data management and replication: a survey

As we delve deeper into the ‘Digital Age’, we witness an explosive growth in the volume, velocity, and variety of the data available on the Internet. For example, in 2012 about 2.5 quintillion bytes of data was created on a daily basis that originated from myriad of sources and applications including mobiledevices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, etc. Such ‘Data Explosions’ has led to one of the most challenging research issues of the current Information and Communication Technology era: how to optimally manage (e.g., store, replicated, filter, and the like) such large amount of data and identify new ways to analyze large amounts of data for unlocking information. It is clear that such large data streams cannot be managed by setting up on-premises enterprise database systems as it leads to a large up-front cost in buying and administering the hardware and software systems. Therefore, next generation data management systems must be deployed on cloud. The cloud computing paradigm provides scalable and elastic resources, such as data and services accessible over the Internet Every Cloud Service Provider must assure that data is efficiently processed and distributed in a way that does not compromise end-users’ Quality of Service (QoS) in terms of data availability, data search delay, data analysis delay, and the like. In the aforementioned perspective, data replication is used in the cloud for improving the performance (e.g., read and write delay) of applications that access data. Through replication a data intensive application or system can achieve high availability, better fault tolerance, and data recovery. In this paper, we survey data management and replication approaches (from 2007 to 2011) that are developed by both industrial and research communities. The focus of the survey is to discuss and characterize the existing approaches of data replication and management that tackle the resource usage and QoS provisioning with different levels of efficiencies. Moreover, the breakdown of both influential expressions (data replication and management) to provide different QoS attributes is deliberated. Furthermore, the performance advantages and disadvantages of data replication and management approaches in the cloud computing environments are analyzed. Open issues and future challenges related to data consistency, scalability, load balancing, processing and placement are also reported.

Malik, Saif Ur Rehman↗

DOE OSTI2017

Integration of end-user Cloud storage for CMS analysis

End-user Cloud storage is increasing rapidly in popularity in research communities thanks to the collaboration capabilities it offers, namely synchronisation and sharing. CERN IT has implemented a model of such storage named, CERNBox, integrated with the CERN AuthN and AuthZ services. To exploit the use of the end-user Cloud storage for the distributed data analysis activity, the CMS experiment has started the integration of CERNBox as a Grid resource. This will allow CMS users to make use of their own storage in the Cloud for their analysis activities as well as to benefit from synchronisation and sharing capabilities to achieve results faster and more effectively. It will provide an integration model of Cloud storages in the Grid, which is implemented and commissioned over the world’s largest computing Grid infrastructure, Worldwide LHC Computing Grid (WLCG). In this paper, we present the integration strategy and infrastructure changes needed in order to transparently integrate end-user Cloud storage with the CMS distributed computing model. We describe the new challenges faced in data management between Grid and Cloud and how they were addressed, along with details of the support for Cloud storage recently introduced into the WLCG data movement middleware, FTS3. Finally, the commissioning experience of CERNBox for the distributed data analysis activity is also presented.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Secure Collaborative Environment for Seamless Sharing of Scientific Knowledge

In a secure collaborative environment, tera-bytes of data generated from powerful scientific instruments are used to train secure machine learning (ML) models on exascale computing systems, which are then securely shared with internal or external collaborators as cloud-based services. Devising such a secure platform is necessary for seamless scientific knowledge sharing without compromising individual, or institute-level, intellectual property and privacy details. By enabling new computing opportunities with sensitive data, we envision a secure collaborative environment that will play a significant role in accelerating scientific discovery. Several recent technological advancements have made it possible to realize these capabilities. In this paper, we present our efforts at ORNL toward developing a secure computation platform. We present a use case where scientific data generated from complex instruments, like those at the Spallation Neutron Source (SNS), are used to train a differential privacy enabled deep learning (DL) network on Summit, which is then hosted as a secure multi-party computation (MPC) service on ORNL’s Compute and Data Environment for Science (CADES) cloud computing platform for third-party inference. In this feasibility study, we discuss the challenges involved, elaborate on leveraged technologies, analyze relevant performance results and present the future vision of our work to establish secure collaboration capabilities within and outside of ORNL.

Yoginath, Srikanth↗

DOE OSTI2026

iDDS: intelligent distributed dispatch and scheduling for workflow orchestration

The intelligent distributed dispatch and scheduling (iDDS) service is a versatile workflow orchestration system designed for large-scale, distributed scientific computing. iDDS extends traditional workload and data management by integrating data-aware execution, conditional logic, and programmable workflows, enabling automation of complex and dynamic processing pipelines. Originally developed for the ATLAS experiment at the large hadron collider, iDDS has evolved into an experiment-agnostic platform that supports both template-driven workflows and a Function-as-a-Task model for Python-based orchestration. This paper presents the architecture and core components of iDDS, highlighting its scalability, modular message-driven design, and integration with systems such as PanDA and Rucio. We demonstrate its versatility through real-world use cases: fine-grained tape resource optimization for ATLAS, orchestration of large Directed Acyclic Graph (DAG) workflows for the Rubin Observatory, distributed hyperparameter optimization for machine learning applications, active learning for physics analyses, and AI-assisted detector design at the electron–ion collider. By unifying workload scheduling, data movement, and adaptive decision-making, iDDS reduces operational overhead and enables reproducible, high-throughput workflows across heterogeneous infrastructures. We conclude with current challenges and future directions, including interactive, cloud-native, and serverless workflow support.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Rahasak—Scalable blockchain architecture for enterprise applications

Blockchain-based decentralized infrastructure has been adapted in various industries to handle the sensitive data in a privacy-preserving manner without trusting third parties. However, integrating state-of-the-art blockchain platforms with the scalable, enterprise-level applications result in several challenges. Current blockchain platforms do not support high transaction throughput, lack high scalability, and cannot provide real-time transaction processing and back-pressure operation handling in high transaction throughput applications(e.g Big data, IoT). In this paper, we propose a novel permissioned blockchain platform “Rahasak” for highly scalable, enterprise applications. Rahasak blockchain adopts the Apache Kafka-based consensus on top of a “Validate-Execute-Group” blockchain architecture to handle realtime transaction execution on the blockchain. The architecture is equipped with a functional programming and actor-based smart contract platform that enables concurrent execution of transactions in the blockchain. Rahasak supports high transaction throughput, high scalability, concurrent transaction execution, data analytics features. Finally, with Rahasak, we make blockchain more scalable, secure, structured and meaningful for further data analytics.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Impact of Data Placement on Resilience in Large-Scale Object Storage Systems

Distributed object storage architectures have become the de facto standard for high-performance storage in big data, cloud, and HPC computing. Object storage deployments using commodity hardware to reduce costs often employ object replication as a method to achieve data resilience. Repairing object replicas after failure is a daunting task for systems with thousands of servers and billions of objects, however, and it is increasingly difficult to evaluate such scenarios at scale on realworld systems. Resilience and availability are both compromised if objects are not repaired in a timely manner. In this work we leverage a high-fidelity discrete-event simulation model to investigate replica reconstruction on large-scale object storage systems with thousands of servers, billions of objects, and petabytes of data. We evaluate the behavior of CRUSH, a well-known object placement algorithm, and identify configuration scenarios in which aggregate rebuild performance is constrained by object placement policies. After determining the root cause of this bottleneck, we then propose enhancements to CRUSH and the usage policies atop it to enable scalable replica reconstruction. We use these methods to demonstrate a simulated aggregate rebuild rate of 410 GiB/s (within 5% of projected ideal linear scaling) on a 1,024-node commodity storage system. We also uncover an unexpected phenomenon in rebuild performance based on the characteristics of the data stored on the system.

Carns, Philip↗

DOE OSTI2011

Towards Composing Data Aware Systems Biology Workflows on Cloud Platforms: A MeDICi-based Approach

Cloud computing is being increasingly adopted for deploying systems biology scientific workflows. Scientists developing these workflows use a wide variety of fragmented and competing data sets and computational tools of all scales to support their research. To this end, the synergy of client side workflow tools with cloud platforms is a promising approach to share and reuse data and workflows. In such systems, the location of data and computation is essential consideration in terms of quality of service for composing a scientific workflow across remote cloud platforms. In this paper, we describe a cloud-based workflow for genome annotation processing that is underpinned by MeDICi - a middleware designed for data intensive scientific applications. The workflow implementation incorporates an execution layer for exploiting data locality that routes the workflow requests to the processing steps that are colocated with the data. We demonstrate our approach by composing two workflowswith the MeDICi pipelines.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Data automation at light sources

Rapidly growing data volumes at light sources demand increasingly automated data collection, distribution, and analysis processes, in order to enable new scientific discoveries while not overwhelming finite human capabilities. We present here the case for automating and outsourcing light source science using cloud-hosted data automation and enrichment services, institutional computing resources, and high- performance computing facilities to provide cost-effective, scalable, and reliable implementations of such processes. We discuss three specific services that accomplish these goals for data distribution, automation, and transformation. In the first, Globus cloud-hosted data automation services are used to implement data capture, distribution, and analysis workflows for Advanced Photon Source and Advanced Light Source beamlines, leveraging institutional storage and computing. In the second, such services are combined with cloud-hosted data indexing and institutional storage to create a collaborative data publication, indexing, and discovery service, the Materials Data Facility (MDF), built to support a host of informatics applications in materials science. The third integrates components of the previous two projects with machine learning capabilities provided by the Data and Learning Hub for science (DLHub) to enable on-demand access to machine learning models from light source data capture and analysis workflows, and provides simplified interfaces to train new models on data from sources such as MDF on leadership scale computing resources. We draw conclusions about best practices for building next-generation data automation systems for future light sources.

Blaiszik, Ben↗

DOE OSTI2024

Assessment of Cloud-based Applications for Enabling a Scalable Riskinformed Predictive Maintenance Strategy

The current light-water reactor fleet uses time-based maintenance strategies to achieve high-capacity factors. But to make nuclear more competitive in the energy market, these reactors could utilize emerging artificial intelligence (AI) and cloud computing technologies to achieve a cost-effective, predictive-maintenance strategy. This paper presents discussion and results on the application of cloud computing in the nuclear industry. The technical viability of cloud computing was analyzed using data from a boiling-water reactor’s safety relief valve. The models were hosted on three different systems: a local personal computer, Idaho National Laboratory’s high-performance computer system, and Microsoft Azure. The data were loaded and processed, and two types of models were trained in an A/B fashion. Based on the speed at which these actions were completed, it was determined that cloud computing affords adequate computing resources. Additionally, the computing power can scale with the demanded load. To enable cloud computing in the existing fleet, additional sensors, networks, and other requirements must be implemented to ensure a smooth transition from current maintenance strategies. However, the benefit is that the plants no longer need to manage their own servers, software, cybersecurity, and information technology support staff for in-house data analytics purpose. Many of these features can be offloaded to the cloud provider for a potential cost savings. Demonstrating how AI can improve the maintenance and operation of non-safety-related systems seems the likely path forward for implementing AI and cloud computing resources inside nuclear power plants.

azure↗

DOE OSTI2015

Intermolecular Network Theory:A General Approach for Understanding the Structural and Dynamic Properties of Liquids and Solutions

Graph theory applications within the physical sciences have a long history. However, it has never moved to the forefront of analytical techniques employed by the computational chemistry community. Though much of the earliest work leveraged percolation theory as a backdrop for understanding critical phenomena and phase transitions within statistical mechanical simulations, modern data science has much to contribute. Here in this paper, we focus on the realm of intermolecular networks, where vertices represent molecules or particles, and an edge represents an intermolecular or interparticle interaction. These interactions are widely understood to dictate many of the physical properties of soft matter, and for liquids in particular, understanding this network is essential. Consider the sheer volume of ~70 years of literature associated with the hydrogen bond network of water, a topic that remains an active area of research even today. The aim of this work is twofold: first, to put into context prior algorithms and analyses that employed “connectedness theory” or lattice-based models to understand the intermolecular networks of chemical systems; and second to discuss a more general strategy for analyzing the multiscale structural and dynamic properties of chemical systems—intermolecular network theory. This approach encompasses foundational methods such as percolation theory, but also utilizes contemporary graph theoretical analyses that have evolved alongside the development of the World Wide Web, cloud computing, and big data. It is the realm of intermolecular network theory to determine the relationships between the essential physics of a system and the topological properties of the network and to derive new techniques in graph theory that can be related to the underlying physico-chemical or reactive properties of a system. Examples are used throughout this chapter that demonstrate applications to a wide variety of complex chemical systems, including ion association within electrolytes, mass transport and the properties of liquid–liquid phase boundaries, and multicomponent solutions.

Complex networks↗