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International Conference for High Performance Computing, Networking, Storage and Analysis (HPC)

作者： Wilson, Ellis Mueller, Frank Pakin, Scott North Carolina State Univ Raleigh NC 27695 USA Los Alamos Natl Lab Los Alamos NM 87545 USA

ISBN: (纸本)9781665454445

This work presents a generalization of NchooseK, a constraint satisfaction system designed to target both quantum circuit devices and quantum annealing devices. Previously, NchooseK supported only hard constraints, which made it suitable for expressing problems in NP (e.g., 3-SAT) but not NP-hard problems (e.g., minimum vertex cover). In this paper we show how support for soft constraints can be added to the model and implementation, broadening the classes of problems that can be expressed elegantly in NchooseK without sacrificing portability across different quantum devices. Through a set of examples, we argue that this enhanced version of NchooseK enables problems to be expressed in a more concise, less error-prone manner than if these problems were encoded manually for quantum execution. We include an empirical evaluation of performance, scalability, and fidelity on both a large IBM Q system and a large D-Wave system.

关键词： circuit-model quantum computing quantum annealing programming models

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Portable Node-Level Parallelism for the PGAS Model

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INTERNATIONAL JOURNAL OF PARALLEL programming 2021年第6期49卷 867-885页

作者： Jungblut, Pascal Fuerlinger, Karl Ludwig Maximilians Univ LMU Munchen Dept Comp Sci MNM Team Oettingenstr 67 D-80538 Munich Germany

The Partitioned Global Address Space (PGAS) programming model brings intuitive shared memory semantics to distributed memory systems. Even with an abstract and unifying virtual global address space it is, however, challenging to use the full potential of different systems. Without explicit support by the implementation node-local operations have to be optimized manually for each architecture. A goal of this work is to offer a user-friendly programming model that provides portable performance across systems. In this paper we present an approach to integrate node-level programming abstractions with the PGAS programming model. We describe the hierarchical data distribution with local patterns and our implementation, MEPHISTO, in C++ using two existing projects. The evaluation of MEPHISTO shows that our approach achieves portable performance while requiring only minimal changes to port it from a CPU-based system to a GPU-based one using a CUDA or HIP back-end.

关键词： PGAS Parallel computing programming models

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Gradual AutoML using Lale 22

Gradual AutoML using Lale

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28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KKD)

作者： Hirzel, Martin Kate, Kiran Ram, Parikshit Shinnar, Avraham Tsay, Jason IBM Res Cambridge MA 02142 USA

ISBN: (纸本)9781450393850

Lale is a sklearn-compatible library for automated machine learning (AutoML). It is open-source (https://***/ibm/lale) and addresses the need for gradual automation of machine learning as opposed to offering a black-box AutoML tool. Black-box AutoML tools are difficult to customize and thus restrict data scientists in leveraging their knowledge and intuition in the automation process. Lale is built on three principles: progressive disclosure, orthogonality, and least surprise. These enable a gradual approach offering a spectrum of usage patterns starting from total automation to controlling almost every aspect of AutoML. Lale provides compositional constructs that let data scientists control some aspects of their pipelines while leaving other aspects free to be searched automatically. This tutorial demonstrates the use of Lale for various machine-learning tasks, showing how to progressively exercise more customization. It also covers AutoML for advanced scenarios such as class imbalance correction, bias detection and mitigation, multi-objective optimization, and working with multi-table datasets. While Lale comes with hyperparameter specifications for 216 operators out-of-the-box, users can also add more operators of their own, and this tutorial covers how to do that. Overall, this tutorial teaches you how you can exercise fine-grained control over AutoML without having to be an AutoML expert.

关键词： programming models AutoML datasets automated data science

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OmpSs@cloudFPGA: An FPGA Task-Based programming Model with Message Passing 36

OmpSs@cloudFPGA: An FPGA Task-Based Programming Model with M...

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36th IEEE International Parallel and Distributed Processing Symposium (IEEE IPDPS)

作者： Miguel de Haro, Juan Cano, Ruben Alvarez, Carlos Jimenez-Gonzalez, Daniel Martorell, Xavier Ayguade, Eduard Labarta, Jeses Abel, Francois Ringlein, Burkhard Weiss, Beat Barcelona Supercomp Ctr Barcelona Spain Univ Politecn Cataluna Barcelona Spain IBM Res Europe Zurich Switzerland

ISBN: (纸本)9781665481069

Nowadays, a new parallel paradigm for energyefficient heterogeneous hardware infrastructures is required to achieve better performance at a reasonable cost on highperformance computing applications. Under this new paradigm, some application parts are offloaded to specialized accelerators that run faster or are more energy-efficient than CPUs. FieldProgrammable Gate Arrays (FPGA) are one of those types of accelerators that are becoming widely available in data centers. This paper proposes OmpSs@cloudFPGA, which includes novel extensions to parallel task-based programming models that enable easy and efficient programming of heterogeneous clusters with FPGAs. The programmer only needs to annotate, with OpenMP-like pragmas, the tasks of the application that should be accelerated in the cluster of FPGAs. Next, the proposed programming model framework automatically extracts parts annotated with High-Level Synthesis (HLS) pragmas and synthesizes them into hardware accelerator cores for FPGAs. Additionally, our extensions include and support two novel features: 1) FPGA-toFPGA direct communication using a Message Passing Interface (MPI) similar Application programming Interface (API) with one-to-one and collective communications to alleviate host communication channel bottleneck, and 2) creating and spawning work from inside the FPGAs to their own accelerator cores based on an MPI rank-like identification. These features break the classical host-accelerator model, where the host (typically the CPU) generates all the work and distributes it to each accelerator. We also present an evaluation of OmpSs@cloudFPGA for different parallel strategies of the N-Body application on the IBM cloudFPGA research platform. Results show that for cluster sizes up to 56 FPGAs, the performance scales linearly. To the best of our knowledge, this is the best performance obtained for N-body over FPGA platforms, reaching 344 Gpairs/s with 56 FPGAs. Finally, we compare the performance and power consu

关键词： FPGA MPI OpenMP programming models network-attached FPGA stand-alone FPGA High-Level Synthesis heterogeneous programming High-performance computing

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Large language model evaluation for high-performance computing software development

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第26期36卷

作者： Godoy, William F. Valero-Lara, Pedro Teranishi, Keita Balaprakash, Prasanna Vetter, Jeffrey S. Oak Ridge Natl Lab Comp & Computat Sci Directorate Oak Ridge TN 37830 USA

We apply AI-assisted large language model (LLM) capabilities of GPT-3 targeting high-performance computing (HPC) kernels for (i) code generation, and (ii) auto-parallelization of serial code in C ++, Fortran, Python and Julia. Our scope includes the following fundamental numerical kernels: AXPY, GEMV, GEMM, SpMV, Jacobi Stencil, and CG, and language/programming models: (1) C++ (e.g., OpenMP [including offload], OpenACC, Kokkos, SyCL, CUDA, and HIP), (2) Fortran (e.g., OpenMP [including offload] and OpenACC), (3) Python (e.g., numpy, Numba, cuPy, and pyCUDA), and (4) Julia (e.g., Threads, ***, ***, and ***). Kernel implementations are generated using GitHub Copilot capabilities powered by the GPT-based OpenAI Codex available in Visual Studio Code given simple + <programming model> + prompt variants. To quantify and compare the generated results, we propose a proficiency metric around the initial 10 suggestions given for each prompt. For auto-parallelization, we use ChatGPT interactively giving simple prompts as in a dialogue with another human including simple "prompt engineering" follow ups. Results suggest that correct outputs for C++ correlate with the adoption and maturity of programming models. For example, OpenMP and CUDA score really high, whereas HIP is still lacking. We found that prompts from either a targeted language such as Fortran or the more general-purpose Python can benefit from adding language keywords, while Julia prompts perform acceptably well for its Threads and *** programming models. We expect to provide an initial quantifiable point of reference for code generation in each programming model using a state-of-the-art LLM. Overall, understanding the convergence of LLMs, AI, and HPC is crucial due to its rapidly evolving nature and how it is redefining human-computer interactions.

关键词： auto-parallelization code generation GPT high-performance computing large language model programming models

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Fuzzy Multi-Objective Optimization to Evaluate the Performance of Suppliers Taking Into Account the Visibility and Supply Chain Risk

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FOUNDATIONS OF COMPUTING AND DECISION SCIENCES 2023年第3期48卷 385-397页

作者： Mukhtadi, Sevdie Alshiqi, Sevdie Opulencia, Maria Jade Catalan Iswanto, A. Heri Alghazali, Tawfeeq Abdulameer Hashim Ghali, Fatima Mira, Mohammed Prakaash, S. Mustafa, Yasser Fakri UNHAN Univ Def Kabupaten Bogor Indonesia Univ Prishtina Hasan Prishtina Econ Fac Prishtina Kosovo Ajman Univ Coll Business Adm Ajman U Arab Emirates Univ Pembangunan Nas Vet Jakarta Fac Hlth Sci Publ Hlth Dept Jakarta Indonesia Islamic Univ English Dept Najaf Iraq Al Ayen Univ Coll Dent Thi Qar Iraq Alfaisal Univ Fac Management Human Resource Management Jeddah Saudi Arabia Panimalar Inst Technol Poonamallee Chennai Dept Math Chennai Tamil Nadu India Univ Mosul Coll Pharm Dept Pharmaceut Chem Mosul 41001 Iraq

Adequate and desirable connections between suppliers and customers necessitate an appropriate flow of information. Therefore, a promising and proper data collaboration in the supply chain is of tremendous significance. Thus, the study's main objective is to provide multiple objective programming models under uncertain conditions to assess the performance of suppliers. To meet that aim, a case study for the reliability assessment of the presented model is carried out. That section is associated with supply chain visibility (SCV). Likewise, the likelihood of unpredicted and undesirable incidents involving supply chain risk (SCR) is taken into consideration. The intimate relation between visibility and risk of the supply chain is deemed efficient for the performance of the supply chain. Incoherence in maximization and minimization of SCR and SCV and other factors, including costs, capacity, or demand, necessitates multiple objective programming models to assess suppliers' performance to accomplish the before-mentioned aims. The study's results indicate the high reliability of the proposed model. Besides, the numeral results reveal that decision-makers in selecting suppliers mainly decrease SCR and then attempt to enhance SCV. In conclusion, the provided model in the study can be a desirable model for analyzing and estimating supplier performance with SCR and SCV simultaneously.

关键词： supply chain visibility suppliers performance supply chain risk information flow programming models

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HPAC: Evaluating Approximate Computing Techniques on HPC OpenMP Applications 21

HPAC: Evaluating Approximate Computing Techniques on HPC Ope...

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International Conference for High Performance Computing, Networking, Storage and Analysis (SC21)

作者： Parasyris, Konstantinos Georgakoudis, Giorgis Menon, Harshitha Diffenderfer, James Laguna, Ignacio Osei-Kuffuor, Daniel Schordan, Markus Lawrence Livermore Natl Lab Livermore CA 94550 USA

ISBN: (纸本)9781450384421

As we approach the limits of Moore's law, researchers are exploring new paradigms for future high-performance computing (HPC) systems. Approximate computing has gained traction by promising to deliver substantial computing power. However, due to the stringent accuracy requirements of HPC scientific applications, the broad adoption of approximate computing methods in HPC requires an in-depth understanding of the application's amenability to approximations. We develop HPAC, a framework with compiler and runtime support for code annotation and transformation, and accuracy vs. performance trade-off analysis of OpenMP HPC applications. We use HPAC to perform an in-depth analysis of the effectiveness of approximate computing techniques when applied to HPC applications. The results reveal possible performance gains of approximation and its interplay with parallel execution. For instance, in the LULESH proxy application approximation provides substantial performance gains due to the reduction of memory accesses. However, in the leukocyte benchmark approximation induces load imbalance in the parallel execution and thus limiting the performance gains.

关键词： Approximate Computing programming models Runtime Systems OpenMP

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The landscape of European policies in the power sector: first-mover advantages

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MITIGATION AND ADAPTATION STRATEGIES FOR GLOBAL CHANGE 2023年第8期28卷 54-54页

作者： Govorukha, Kristina Mayer, Philip Ruebbelke, Dirk Tech Univ Bergakad Freiberg Schlossplatz 1 D-09599 Freiberg Germany Ruhr Univ Bochum Ctr Environm Management Resources & Energy D-44801 Bochum Germany

In order to achieve the commonly agreed emission reduction target, the European Commission called upon the member states to submit National Energy and Climate Plans to ensure increased transparency for the respective national targets and strategies. An analysis of these plans shows that some member states have declared ambitious emission reductions targets, as well as technology-specific phaseout policies in the power sector. A transformation to a climate-friendly system requires considerable investment, the question arises if there are benefits to be in the vanguard. We find that countries may have an incentive to outperform in the development of a low-carbon electricity system.

关键词： Electricity Utilities Thermal generation Unilateral action Climate policy C6 Mathematical methods programming models Mathematical and simulation modeling Q4 Electricity Energy Energy Utilities Gas Hydrocarbons

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Performance and Portability of a Linear Solver Across Emerging Architectures 7th

Performance and Portability of a Linear Solver Across Emergi...

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7th International Workshop on Accelerator programming using Directives (WACCPD)

作者： Walden, Aaron C. Zubair, Mohammad Nielsen, Eric J. NASA Langley Res Ctr Hampton VA 23665 USA Old Dominion Univ Norfolk VA USA

ISBN: (纸本)9783030742232;9783030742249

A linear solver algorithm used by a large-scale unstructured-grid computational fluid dynamics application is examined for a broad range of familiar and emerging architectures. Efficient implementation of a linear solver is challenging on recent CPUs offering vector architectures. Vector loads and stores are essential to effectively utilize available memory bandwidth on CPUs, and maintaining performance across different CPUs can be difficult in the face of varying vector lengths offered by each. A similar challenge occurs on GPU architectures, where it is essential to have coalesced memory accesses to utilize memory bandwidth effectively. In this work, we demonstrate that restructuring a computation, and possibly data layout, with regard to architecture is essential to achieve optimal performance by establishing a performance benchmark for each target architecture in a low level language such as vector intrinsics or CUDA. In doing so, we demonstrate how a linear solver kernel can be mapped to Intel((R)) Xeon (TM) and Xeon Phi (TM), Marvell((R)) ThunderX2((R)), NEC (R) SX-Aurora (TM) TSUBASA Vector Engine, and NVIDIA((R)) and AMD((R)) GPUs. We further demonstrate that the required code restructuring can be achieved in higher level programming environments such as OpenACC, OCCA, and Intel((R)) OneAPI (TM)/SYCL, and that each generally results in optimal performance on the target architecture. Relative performance metrics for all implementations are shown, and subjective ratings for ease of implementation and optimization are suggested.

关键词： programming models Performance portability Emerging architecture CFD HPC CUDA OpenACC OCCA AVX-512 intrinsics Neon intrinsics Arm GPU V100 A100 MI50 Xeon Phi SX-Aurora ThunderX2

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Mapping Constraint Problems onto Quantum Gate and Annealing Devices 2

Mapping Constraint Problems onto Quantum Gate and Annealing ...

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2nd International Workshop on Quantum Computing Software (QCS)

作者： Wilson, Ellis Mueller, Frank Pakin, Scott North Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA Los Alamos Natl Lab Comp Computat & Stat Sci Div Los Alamos NM 87545 USA

ISBN: (纸本)9781728186740

This work presents NchooseK, a unified programming model for constraint satisfaction problems that can be mapped to both quantum circuit and annealing devices through Quadratic Unconstrained Binary Operators (QUBOs). Our mapping provides an approachable and effective way to program both types of quantum computers. We provide examples of NchooseK being used.

关键词： circuit-model quantum computing quantum annealing programming models

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