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Chandrasekaran, Sunita

Publications and source records attributed to Chandrasekaran, Sunita.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

CI/CD Efforts for Validation, Verification and Benchmarking OpenMP Implementations

Software developers must adapt to keep up with the changing capabilities of platforms so that they can utilize the power of High-Performance Computers (HPC), including exascale systems. OpenMP, a directive-based parallel programming model, allows developers to include directives to existing C, C++, or Fortran code to allow node level parallelism without compromising performance. This paper describes our CI/CD efforts to provide easy evaluation of the support of OpenMP across different compilers using existing testsuites and benchmark suites on HPC platforms. Our main contributions include (1) the set of a Continuous Integration (CI) and Continuous Development (CD) workflow that captures bugs and provides faster feedback to compiler developers, (2) an evaluation of OpenMP (offloading) implementations supported by AMD, HPE, GNU, LLVM, and Intel, and (3) evaluation of the quality of compilers across different heterogeneous HPC platforms. With the comprehensive testing through the CI/CD workflow, we aim to provide a comprehensive understanding of the current state of OpenMP (offloading) support in different compilers and heterogeneous platforms consisting of CPUs and GPUs from NVIDIA, AMD, and Intel.

Jarmusch, Aaron↗
DOE OSTI2024

UNNT: A novel Utility for comparing Neural Net and Tree-based models

The use of deep learning (DL) is steadily gaining traction in scientific challenges such as cancer research. Advances in enhanced data generation, machine learning algorithms, and compute infrastructure have led to an acceleration in the use of deep learning in various domains of cancer research such as drug response problems. In our study, we explored tree-based models to improve the accuracy of a single drug response model and demonstrate that tree-based models such as XGBoost (eXtreme Gradient Boosting) have advantages over deep learning models, such as a convolutional neural network (CNN), for single drug response problems. However, comparing models is not a trivial task. To make training and comparing CNNs and XGBoost more accessible to users, we developed an open-source library called UNNT (A novel Utility for comparing Neural Net and Tree-based models). The case studies, in this manuscript, focus on cancer drug response datasets however the application can be used on datasets from other domains, such as chemistry.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A Cast of Thousands: How the IDEAS Productivity Project Has Advanced Software Productivity and Sustainability

Computational and data-enabled science and engineering are revolutionizing advances throughout science and society, at all scales of computing. For example, teams in the U.S. Department of Energy’s Exascale Computing Project have been tackling new frontiers in modeling, simulation, and analysis by exploiting unprecedented exascale computing capabilities—building an advanced software ecosystem that supports next-generation applications and addresses disruptive changes in computer architectures. However, concerns are growing about the productivity of the developers of scientific software. Members of the Interoperable Design of Extreme-scale Application Software project serve as catalysts to address these challenges through fostering software communities, incubating and curating methodologies and resources, and disseminating knowledge to advance developer productivity and software sustainability. This article discusses how these synergistic activities are advancing scientific discovery—mitigating technical risks by building a firmer foundation for reproducible, sustainable science at all scales of computing, from laptops to clusters to exascale and beyond.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Frontier: Exploring Exascale

As the US Department of Energy (DOE) computing facilities began deploying petascale systems in 2008, DOE was already setting its sights on exascale. In that year, DARPA published a report on the feasibility of reaching exascale. The report authors identified several key challenges in the pursuit of exascale including power, memory, concurrency, and resiliency. That report informed the DOE's computing strategy for reaching exascale. With the deployment of Oak Ridge National Laboratory's Frontier supercomputer, we have officially entered the exascale era. In this paper, we discuss Frontier's architecture, how it addresses those challenges, and describe some early application results from Oak Ridge Leadership Computing Facility's Center of Excellence and the Exascale Computing Project.

Atchley, Scott {Leadership Computing}↗
DOE OSTI2023

EZ: An efficient, charge conserving current deposition algorithm for electromagnetic particle-in-cell simulations

Here, we present EZ, a novel current deposition algorithm for particle-in-cell (PIC) simulations. EZ calculates the current density on the electromagnetic grid due to macro-particle motion within a time step by solving the continuity equation of electrodynamics. Being a charge conserving hybridization of Esirkepov's method and ZigZag, we refer to it as “EZ” as shorthand for “Esirkepov meets ZigZag”. Simulations of a warm, relativistic plasma with PIConGPU show that EZ achieves the same level of charge conservation as the commonly used method by Esirkepov, yet reaches higher performance for macro-particle assignment-functions up to third-order. In addition to a detailed description of the functioning of EZ, reasons for the expected and observed performance increase are given, and guidelines for its implementation aiming at highest performance on GPUs are provided.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Application Experiences on a GPU-Accelerated Arm-based HPC Testbed

This paper assesses and reports the experience of ten teams working to port, validate, and benchmark several High Performance Computing applications on a novel GPU-accelerated Arm testbed system. The testbed consists of eight NVIDIA Arm HPC Developer Kit systems, each one equipped with a server-class Arm CPU from Ampere Computing and two data center GPUs from NVIDIA Corp. The systems are connected together using InfiniBand interconnect. The selected applications and mini-apps are written using several programming languages and use multiple accelerator-based programming models for GPUs such as CUDA, OpenACC, and OpenMP offloading. Working on application porting requires a robust and easy-to-access programming environment, including a variety of compilers and optimized scientific libraries. The goal of this work is to evaluate platform readiness and assess the effort required from developers to deploy well-established scientific workloads on current and future generation Arm-based GPU-accelerated HPC systems. The reported case studies demonstrate that the current level of maturity and diversity of software and tools is already adequate for large-scale production deployments.

Elwasif, Wael↗
DOE OSTI2022

ECP SOLLVE: Validation and Verification Testsuite Status Update and Compiler Insight for OpenMP

The OpenMP language continues to evolve with every new specification release, as does the need to validate and verify the new features that have been implemented by the different vendors. With the release of OpenMP 5.0 and OpenMP 5.1, new target offload and host-based features have been introduced to the programming model. While OpenMP continues to grow in maturity, there is an observable growth in the number of compiler and hardware vendors that support OpenMP. In this manuscript, the main focus is on evaluating the conformity and OpenMP implementation progress of various compiler vendors such as Cray, IBM, GNU, Clang/LLVM, NVIDIA, and Intel. More specifically, the 4.5, 5.0, and 5.1 versions of the OpenMP specification are analyzed. For our experimental setup, the Crusher and Summit computing systems hosted by Oak Ridge National Lab’s Computing Facilities are utilized. The effort of vendor agnostic analysis of these implementations is especially valuable for application developers who are using new OpenMP features to accelerate their scientific codes. Insights are presented into the current implementation status of various vendors, the progression of specific compiler’s support for OpenMP overtime, the subset of OpenMP 4.5, 5.0, and 5.1 that is supported by all compilers, and examples of how our test suite has influenced discussion regarding the correct interpretation of the OpenMP specification. By evaluating OpenMP conformity of pre-Exascale computing systems, the aim is to detail progress and status of AMD + Cray ecosystem before the system and their OpenMP implementation is used for mission critical applications when the first Exascale Computer Frontier is made available to applications.

Huber, Thomas↗
DOE OSTI2022

Analysis of Validating and Verifying OpenACC Compilers 3.0 and Above

OpenACC is a high-level directive-based parallel programming model that can manage the sophistication of heterogeneity in architectures and abstract it from the users. The portability of the model across CPUs and accelerators has gained the model a wide variety of users. This means it is also crucial to analyze the reliability of the compilers’ implementations. To address this challenge, the OpenACC Validation and Verification team has proposed a validation testsuite to verify the OpenACC implementations across various compilers with an infrastructure for a more streamlined execution. This paper will cover the following aspects: (a) the new developments since the last publication on the testsuite, (b) outline the use of the infrastructure, (c) discuss tests that highlight our workflow process, (d) analyze the results from executing the testsuite on various systems, and (e) outline future developments.

Jarmusch, Aaron↗
DOE OSTI2021

Performance Analysis of PIConGPU: Particle-in-Cell on GPUs using NVIDIA’s NSight Systems and NSight Compute

PIConGPU, Particle In Cell on GPUs, is an open source simulations framework for plasma and laser-plasma physics used to develop advanced particle accelerators for radiation therapy of cancer, high energy physics and photon science. While PIConGPU has been optimized for at least 5 years to run well on NVIDIA GPU-based clusters, there has been limited exploration by the development team of potential scalability bottlenecks using recently updated and new tools including NVIDIA’s NVProf tool and the brand-new NVIDIA NSight Suite (Systems and Compute) tools. PIConGPU is a highly optimized application that runs production jobs at scale on a system Oak Ridge Leadership Facility’s (OLCF) Summit supercomputer (using the full machine at 4600 nodes; at 98% of GPU utilization on all ~28000 NVIDIA Volta GPUs). PIConGPU has been selected as one of the the eight applications for OLCF’s coveted Center for Accelerated Application Readiness (CAAR) program aimed at the facility’s Frontier supercomputer (OLCF’s first exascale system to launch in 2021), to partner with our vendors (primary vendors: AMD and Cray/HPE) ensuring that Frontier will be able to perform large-scale science when it opens to users in 2022. To this effect, performance engineers on the PIConGPU team wanted to dive deep into the application to understand at the finest granularity, which portions of the code could be further optimized to exploit the hardware on Summit at it’s maximum potential and also to elucidate which key kernels should be tracked and optimized for the CAAR effort to port this code to Frontier. Any bottlenecks that are observed via performance profiling on Summit are likely to also impact scalability on the Frontier-dev system and the Frontier Early Access (EA) system. Additionally, the engineers wanted to take a closer look at the newest NVIDIA profiling tools which allows us to identify the most useful features on these tools and will provide an opportunity to compare it to new AMD and Cray’s performance analysis tool releases and provide feedback to our vendor partners on what features are most important and mission critical for CAAR efforts. The primary goal of this report is to focus on the evaluation of PIConGPU’s most time-intensive kernels using NVProf and NSight Suite. Three kernels, Current Deposition (also known as Compute Current), Particle Push (Move and Mark), and Shift Particles are known to be some of the most time-consuming kernels in PIConGPU. The Current xi Deposition kernel and Particle Push kernel both set up the particle attributes for running any physics simulation with PIConGPU, so it is crucial to improve the performance of these two kernels. In this report, we measure single GPU metrics for the three kernels, offer high level takeaways from the conducted analysis, and compare the profiling data from NSight Compute to that of NVProf. This analysis was performed using a grid size of 240 x 272 x 224, and 10 time steps with the Mid-November Figure of Merit (FOM) run setup. The Traveling Wave Electron Acceleration (TWEAC) science case used in this run is a representative science case for PIConGPU. This execution can also be used for baseline analysis on AMD MI50/ MI60 systems. As of the time of writing, the PIConGPU application has limited use for features of NSight Systems, so this report will mainly focus on insights garnered from NSight Compute. For this analysis, we run the “full” metric set available in NSight Compute version 2020.1.2 and use NSight Systems version 2020.3.1 to generate the application timeline.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Performance Assessment of OpenMP Compilers Targeting NVIDIA V100 GPUs

Heterogeneous systems are becoming increasingly prevalent. In order to exploit the rich compute resources of such systems, robust programming models are needed for application developers to seamlessly migrate legacy code from today’s systems to tomorrow’s. Over the past decade and more, directives have been established as one of the promising paths to tackle programmatic challenges on emerging systems. This work focuses on applying and demonstrating OpenMP offloading directives on five proxy applications. We observe that the performance varies widely from one compiler to the other; a crucial aspect of our work is reporting best practices to application developers who use OpenMP offloading compilers. While some issues can be worked around by the developer, there are other issues that must be reported to the compiler vendors. By restructuring OpenMP offloading directives, we gain an 18x speedup for the su3 proxy application on NERSC’s Cori system when using the Clang compiler, and a 15.7x speedup by switching max reductions to add reductions in the laplace mini-app when using the Cray-llvm compiler on Cori.

Davis, Josh↗
DOE OSTI2019

Analysis of OpenMP 4.5 Offloading in Implementations: Correctness and Overhead

The OpenMP language features have been evolving to meet the rapid development in hardware platforms. This journal focuses on evaluating implementations of OpenMP 4.5 target offload features in compilers such as Clang, XL and GCC that are an integral part of the software harness on supercomputers and clusters. We use Summit (Top supercomputer in the world as of November 2018) as one of our experimental setup. Such an effort is particularly critical on such supercomputers as that is being widely used by application developers to run their scientific codes at scale. Our tests not only evaluate the OpenMP implementations but also expose ambiguities within the OpenMP 4.5 specification. We also assess the overhead of the different OpenMP runtimes in relationship to the different directives and clauses. 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 Summit is advertised as having only partial OpenMP 4.x support. This is a synergistic effort to help identify and fix bugs in features’ implementations that are required by applications and prevent deployment delays later on. Going forward, we also plan to interact with standard benchmarking organizations like SPEC/HPG to donate our tests and mini-apps/kernels for potential inclusion in the next release versions of SPEC benchmark suite.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

MPI + OpenACC: Accelerating radiation transport mini-application, minisweep, on heterogeneous systems

According to the article, architectures are rapidly evolving, and exascale machines are expected to offer billion-way concurrency. We need to rethink algorithms, languages and programming models among other components in order to migrate large scale applications and explore parallelism on these machines. Although directive-based programming models allow programmers to worry less about programming and more about science, expressing complex parallel patterns in these models can be a daunting task especially when the goal is to match the performance that the hardware platforms can offer. One such pattern is wavefront. This paper extensively studies a wavefront-based miniapplication for Denovo, a production code for nuclear reactor modeling. We parallelize the Koch–Baker–Alcouffe (KBA) parallel-wavefront sweep algorithm in the main kernel of Minisweep (the miniapplication) using CUDA 9.0, OpenMP 4.0 (SIMD) and OpenACC 2.6. Our OpenACC implementation running on NVIDIA’s next-generation Volta GPU boasts an 85.06x speedup over serial code, which is larger than CUDA’s 83.72x speedup over the same serial implementation. We also explore the scalability of our solution using MPI to decompose our simulation domain, allowing us to run on many nodes and accelerators present in state-of-the-art HPC systems. Our parallelization effort across platforms also motivated us to define an abstract parallelism model that is architecture independent, with a goal of creating software abstractions that can be used by applications employing the wavefront sweep motif.

Computer Science↗
DOE OSTI2018

OpenMP 4.5 Validation and Verification Suite for Device Offload

OpenMP has been widely adopted for shared memory systems for over a decade. With the heterogeneity trend in architectures rapidly growing, the programming model needed to evolve such that applications could not only be ported to traditional CPUs but also to accelerators often acting as discrete or integrated devices to CPUs. To that end, OpenMP started to provide support for heterogeneous systems since 2013 when the version 4.0 of the specification was ratified. OpenMP 4.5 is being enhanced to cover major requirements of Exascale Computing Project (ECP) applications. As a result it is time-critical to ensure that the implementations of the 4.5 features are correct and conforming to the specification. This paper focuses on building a Validation and Verification testsuite that will test and present results for several offloading features implemented in compilers such as Clang, IBM XL C/C++, CCE, and GCC. We have results for our testsuite on TITAN, Summitdev and Summit at the Oak Ridge National Lab. We will highlight some of the ambiguities we encountered in the process of validating and verifying feature implementations. We also make the testsuite available for anyone to use and will walk the readers through the infrastructure and the workflow of the testsuite. A website has been built to capture our efforts narrated in this paper https://crpl.cis.udel.edu/ompvvsollve.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Evaluating Support for OpenMP Offload Features

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.

Monsalve Diaz, Jose↗
DOE OSTI2018

OpenMP 4.5 Validation and Verification Suite for Device Offload

OpenMP has been widely adopted for shared memory systems for over a decade. With the heterogeneity trend in architectures rapidly growing, the programming model needed to evolve such that applications could not only be ported to traditional CPUs but also to accelerators often acting as discrete or integrated devices to CPUs. To that end, OpenMP started to provide support for heterogeneous systems since 2013 when the version 4.0 of the specification was ratified. OpenMP 4.5 is being enhanced to cover major requirements of Exascale Computing Project (ECP) applications. As a result it is time-critical to ensure that the implementations of the 4.5 features are correct and conforming to the specification. This paper focuses on building a Validation and Verification testsuite that will test and present results for several offloading features implemented in compilers such as Clang, IBM XL C/C++, CCE, and GCC. We have results for our testsuite on TITAN, Summitdev and Summit at the Oak Ridge National Lab. We will highlight some of the ambiguities we encountered in the process of validating and verifying feature implementations. We also make the testsuite available for anyone to use and will walk the readers through the infrastructure and the workflow of the testsuite. A website has been built to capture our efforts narrated in this paper https://crpl.cis.udel.edu/ompvvsollve.

Monsalve Diaz, Jose↗
DOE OSTI2018

The OpenACC data model: Preliminary study on its major challenges and implementations

This paper describes how the OpenACC data model is implemented in current OpenACC compilers, ranging from research compilers (OpenUH and OpenARC) to a commercial compiler (the PGI OpenACC compiler). First, we summarize various memory architectures in today’s accelerator systems. We then describe details and issues in implementing the OpenACC data model in three different OpenACC compilers. This includes managing page tables, asynchronous data transfers, asynchronous memory allocate and free, host data construct, aliasing on a data directive, reusing device memory, partially present data, and adjacent data. We also discusses ongoing work to manage large, complex dynamic data structures. We measured the present table lookups, device memory allocation, pinned memory allocation, and managed memory in the three OpenACC compilers using eight OpenACC applications (seven from the SPEC ACCEL benchmark suite and a shock-hydrodynamics mini-application called LULESH).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Abstractions and Directives for Adapting Wavefront Algorithms to Future Architectures

Architectures are rapidly evolving, and exascale machines are expected to offer billion-way concurrency. We need to rethink algorithms, languages and programming models among other components in order to migrate large scale applications and explore parallelism on these machines. Although directive-based programming models allow programmers to worry less about programming and more about science, expressing complex parallel patterns in these models can be a daunting task especially when the goal is to match the performance that the hardware platforms can offer. One such pattern is wavefront. This paper extensively studies a wavefront-based miniapplication for Denovo, a production code for nuclear reactor modeling. We parallelize the Koch-Baker-Alcouffe (KBA) parallel-wavefront sweep algorithm in the main kernel of Minisweep (the miniapplication) using CUDA, OpenMP and OpenACC. Our OpenACC implementation running on NVIDIA's next-generation Volta GPU boasts an 85.06x speedup over serial code, which is larger than CUDA's 83.72x speedup over the same serial implementation. Our experimental platform includes SummitDev, an ORNL representative architecture of the upcoming Summit supercomputer. Our parallelization effort across platforms also motivated us to define an abstract parallelism model that is architecture independent, with a goal of creating software abstractions that can be used by applications employing the wavefront sweep motif.

Searles, Robert↗