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检索条件"任意字段=2021 IEEE/ACM Workshop on Programming Environments for Heterogeneous Computing, PEHC 2021"

共 19 条记录，以下是11-20 订阅

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Near-Data FPGA-Accelerated Processing of Collective and Inference Operations in Disaggregated Memory Systems 7

Near-Data FPGA-Accelerated Processing of Collective and Infe...

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ieee/acm 7th International workshop on heterogeneous High- Performance Reconfigurable computing (H2RC) Part of International Conference for High Performance computing, Networking, Storage and Analysis (SC)

作者： Heinz, Carsten Koch, Andreas Tech Univ Darmstadt Embedded Syst & Applicat Grp Darmstadt Germany

ISBN: (纸本)9781665446648

With growing data set sizes, many scientific and data center HPC workloads observe an increasing scaling imbalance, e.g., between compute and memory capacities. As a solution, disaggregated system architectures employ spatial distribution of the different resources. They aim for independent scaling of the different resource kinds (e.g., compute, non-volatile storage, memory), and use fast communication fabrics for their interconnection. However, for some bulk operations, such as reductions and collections, it is still beneficial to perform them close to the memories, avoiding the need to move large volumes of data over the fabric. This work realizes a disaggregated system capable of performing such near-data processing (NDP) operations by extending the distributed memory controllers with hardware-accelerated compute capabilities. The actual computations execute on FPGAs and can be abstractly described using C/C++ as compilable by high-level hardware synthesis (HLS) tools. We have aimed for high usability of our technology also by HPC experts unfamiliar with hardware design. An automated toolflow encapsulates the creation and deployment of the actual accelerators in the disaggregated system. The NDP operations execute distributed across all memory nodes, and are easily accessed using a simple MPI-based programming interface that requires only minimal effort to use in existing applications. Our solution is demonstrated using a prototype disaggregated system based on the low-latency EXTOLL fabric for communication. We evaluate both conventional reductions/collectives as well as complete machine-learning inference tasks.

关键词： Nonvolatile memory Memory management Systems architecture Prototypes Process control programming Hardware

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Empowering Visual Internet-of-Things Mashups with Self-Healing Capabilities 3

Empowering Visual Internet-of-Things Mashups with Self-Heali...

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3rd ieee/acm International workshop on Software Engineering Research and Practices for the IoT (SERP4IoT)

作者： Dias, Joao Pedro Restivo, Andre Ferreira, Hugo Sereno Univ Porto Fac Engn DEI Porto Portugal INESC TEC Porto Portugal LIACC Porto Portugal

ISBN: (纸本)9781665445696

Internet-of-Things (IoT) systems have spread among different application domains, from home automation to industrial manufacturing processes. The rushed development by competing vendors to meet the market demand of IoT solutions, the lack of interoperability standards, and the overall lack of a defined set of best practices have resulted in a highly complex, heterogeneous, and frangible ecosystem. Several works have been pushing towards visual programming solutions to abstract the underlying complexity and help humans reason about it. As these solutions begin to meet widespread adoption, their building blocks usually do not consider reliability issues. Node-RED, being one of the most popular tools, also lacks such mechanisms, either built-in or via extensions. In this work we present SHEN (Self-Healing Extensions for Node-RED) which provides 17 nodes that collectively enable the implementation of self-healing strategies within this visual framework. We proceed to demonstrate the feasibility and effectiveness of the approach using real devices and fault injection techniques.

关键词： Internet-of-Things Self-Healing Autonomic computing Visual programming Dependability

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Measurement and Analysis of GPU-Accelerated OpenCL Computations on Intel GPUs 3

Measurement and Analysis of GPU-Accelerated OpenCL Computati...

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ieee/acm International workshop on programming and Performance Visualization Tools (ProTools)

作者： Cherian, Aaron Thomas Zhou, Keren Grubisic, Dejan Meng, Xiaozhu Mellor-Crummey, John Rice Univ Dept Comp Sci Houston TX 77251 USA

ISBN: (纸本)9781665411103

Graphics Processing Units (GPUs) have become a key technology for accelerating node performance in supercomputers, including the US Department of Energy's forthcoming exascale systems. Since the execution model for GPUs differs from that for conventional processors, applications need to be rewritten to exploit GPU parallelism. Performance tools are needed for such GPU-accelerated systems to help developers assess how well applications offload computation onto GPUs. In this paper, we describe extensions to Rice University's HPCToolkit performance tools that support measurement and analysis of Intel's DPC++ programming model for GPU-accelerated systems atop an implementation of the industry-standard OpenCL framework for heterogeneous parallelism on Intel GPUs. HPCToolkit supports three techniques for performance analysis of programs atop OpenCL on Intel GPUs. First, HPCToolkit supports profiling and tracing of OpenCL kernels. Second, HPCToolkit supports CPU-GPU blame shifting for OpenCL kernel executions-a profiling technique that can identify code that executes on one or more CPUs while GPUs are idle. Third, HPCToolkit supports fine-grained measurement, analysis, and attribution of performance metrics to OpenCL GPU kernels, including instruction counts, execution latency, and SIMD waste. The paper describes these capabilities and then illustrates their application in case studies with two applications that offload computations onto Intel GPUs.

关键词： Supercomputers High performance computing Performance analysis Parallel programming

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Proceedings of EduHPC 2021: workshop on Education for High-Performance computing, Held in conjunction with SC 2021: The International Conference for High Performance computing, Networking, Storage and Analysis

Proceedings of EduHPC 2021: Workshop on Education for High-P...

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9th ieee/acm workshop on Education for High-Performance computing, EduHPC 2021

ISBN: (纸本)9781665411165

The proceedings contain 7 papers. The topics discussed include: experience and practice teaching an undergraduate course on diverse heterogeneous architectures;the hour of cyberinfrastructure (hour of ci): early findings from pilot study to build cyber literacy for GIScience;educating HPC users in the use of advanced computing technology;visualizing parallel dynamic programming using the thread safe graphics library;automating testing of visual observed concurrency;lightning talks of EDuHPC 2021;and peachy parallel assignments (EDuHPC 2021).

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TRIPP: Transparent Resource Provisioning for Multi-Tenant CPU-GPU based Cloud environments 11

TRIPP: Transparent Resource Provisioning for Multi-Tenant CP...

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11th Brazilian Symposium on computing Systems Engineering (SBESC)

作者： Vicenzi, Julio Costella Knorst, Tiago Jordan, Michael G. Korol, Guilherme Schneider Beck, Antonio Carlos Rutzig, Mateus Beck Univ Fed Rio Grande do Sul UFRGS Inst Informat PGMICRO Porto Alegre RS Brazil Univ Fed Rio Grande do Sul UFRGS Inst Informat Porto Alegre RS Brazil Univ Fed Santa Maria UFSM Elect & Comp Dept Santa Maria RS Brazil

ISBN: (纸本)9781665443111

Cloud Warehouses have been expanding their computational resources to cover the growing offloading of tenants' applications. Currently, cloud nodes integrate heterogeneous resources, such as CPU and GPU, so they can exploit different types and levels of parallelism available in the applications. However, heterogeneous cloud nodes bring challenges to the software development process, since the programmer must be aware of each device's specifications, analyze and distribute the code over the available devices. Even though OpenCL supports transparent programming on heterogeneous devices, softening the programmer's burden, the choice of target device is still the programmer's responsibility. Given that, this work proposes a framework for the execution of OpenCL applications on a multi-tenant CPU-GPU cloud environment, responsible for transparently scheduling the applications to the best available device, without any interaction from the programmer. The framework has the goal of optimizing resource provisioning, reducing makespan and energy consumption. Considering the execution of the PolyBench benchmark suite, the framework shows reduction on makespan of 3.4x and energy savings of 33% when compared to the GPU standalone execution.

关键词： OpenCL Cloud computing heterogeneous Systems Parallel programming

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Mamba: Portable Array-based Abstractions for heterogeneous High-Performance Systems

Mamba: Portable Array-based Abstractions for Heterogeneous H...

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International workshop on Performance, Portability and Productivity in HPC (P3HPC)

作者： Dykes, Tim Foyer, Clement Richardson, Harvey Svedin, Martin Podobas, Artur Jansson, Niclas Markidis, Stefano Tate, Adrian McIntosh-Smith, Simon HPE HPC AI EMEA Res Lab Bristol Avon England KTH Royal Inst Technol Stockholm Sweden Numer Algorithms Grp Ltd NAG Oxford England Univ Bristol HPC Res Grp Bristol Avon England

ISBN: (纸本)9781665424394

High performance computing architectures have become increasingly heterogeneous in recent times. This growing architectural variety presents a multi-faceted portability problem affecting applications, libraries, programming models, languages, compilers, run-times, and system software. Approaches for performance portability typically focus heavily on efficient usage of parallel compute architectures and less on data locality abstractions and complex memory systems, with minimal support afforded to effective memory management in traditional HPC languages such as C and Fortran. We present Mamba, a library to facilitate usage of heterogeneous memory systems by high performance application/library developers through high level array-based abstractions for memory management supported by a low-level generic memory API. We detail the library design and implementation, demonstrating generic memory allocation, data layout specification, array tiling and heterogeneous transport. We evaluate performance in the context of a typical matrix transposition, DNA sequencing benchmark, and an application use case for high-order spectral element based incompressible flow.

关键词： Memory Management Performance Portability Abstraction

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Case study of SARS-CoV-2 transmission risk assessment in indoor environments using cloud computing resources

Case study of SARS-CoV-2 transmission risk assessment in ind...

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作者： Saurabh, Kumar Adavani, Santi Tan, Kendrick Ishii, Masado Gao, Boshun Krishnamurthy, Adarsh Sundar, Hari Ganapathysubramanian, Baskar Iowa State Univ Ames IA 50011 USA RocketML Inc Beaverton OR USA Univ Utah Salt Lake City UT 84112 USA

ISBN: (纸本)9781665483889

Complex flow simulations are conventionally performed on HPC clusters. However, the limited availability of HPC resources and steep learning curve of executing on traditional supercomputer infrastructure has drawn attention towards deploying flow simulation software on the cloud. We showcase how a complex computational framework-that can evaluate COVID-19 transmission risk in various indoor classroom scenarios-can be abstracted and deployed on cloud services. The availability of such cloud-based personalized planning tools can enable educational institutions, medical institutions, public sector workers (courthouses, police stations, airports, etc.), and other entities to comprehensively evaluate various in-person interaction scenarios for transmission risk. We deploy the simulation framework on the Azure cloud framework, utilizing the DENDRO-KT mesh generation tool and PETSc solvers. The cloud abstraction is provided by RocketML cloud infrastructure. We compare the performance of the cloud machines with state-of-the-art HPC machine TACC Frontera. Our results suggest that cloud-based HPC resources are a viable strategy for a diverse array of end-users to rapidly and efficiently deploy simulation software.

关键词： Risk perception

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GPU Acceleration of the FINE/FR CFD Solver in a heterogeneous Environment with OpenACC Directives 7th

GPU Acceleration of the FINE/FR CFD Solver in a Heterogeneou...

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

作者： Zhai, X. M. Shine Gutzwiller, David Puri, Kunal Hirsch, Charles Numeca USA 1044 Larkin St San Francisco CA 94109 USA Numeca Int Chaussee Hulpe 189 B-1170 Brussels Belgium

ISBN: (纸本)9783030742232;9783030742249

OpenACC has been highly successful in adapting legacy CPU-only applications for modern heterogeneous computing environments equipped with GPUs, as demonstrated by many projects as well as our previous experience. In this work, OpenACC is leveraged to transform another Computational Fluid Dynamics (CFD) high order solver FINE/FR to be GPU-eligible. On the Summit supercomputer, impressive GPU speedup ranging from 6X to 80X has been achieved using up to 12,288 GPUs. Techniques critical to achieving good speedup include aggressive reduction of data transfers between CPUs and GPUs, and optimizations targeted at improving exposed parallelism to GPUs. We have demonstrated that OpenACC offers an efficient, portable and easily-maintainable approach to achieve fast turnaround time for high-fidelity industrial simulations.

关键词： heterogeneous computing OpenACC Computational Fluid Dynamics

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Distributed federated service chaining for heterogeneous network environments 21

Distributed federated service chaining for heterogeneous net...

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14th ieee/acm International Conference on Utility and Cloud computing, UCC 2021

作者： Chen, Chen Nagel, Lars Cui, Lin Tso, Fung Po Loughborough University Loughborough United Kingdom Jinan University Guangzhou China

ISBN: (纸本)9781450385640

Future networks are expected to support cross-domain, cost-aware and fine-grained services in an efficient and flexible manner. Service Function Chaining (SFC) has been introduced as a promising approach to deliver these services. In the literature, centralized resource orchestration is usually employed to process SFC requests and manage computing and network resources. However, centralized approaches inhibit the scalability and domain autonomy in multi-domain networks. They also neglect location and hardware dependencies of service chains. In this paper, we propose federated service chaining, a distributed framework which orchestrates and maintains the SFC placement while sharing a minimal amount of domain information and control. We first formulate a deployment cost minimization problem as an Integer Linear programming (ILP) problem with fine-grained constraints for location and hardware dependencies, which is NP-hard. We then devise a Distributed Federated Service Chaining placement approach (DFSC) using inter-domain paths and border nodes information. Our extensive experiments demonstrate that DFSC efficiently optimizes the deployment cost, supports domain autonomy and enables faster decision-making. The results show that DFSC finds solutions within a factor 1.15 of the optimal solution. Compared to a centralized approach in the literature, DFSC reduces the deployment cost by 12% while being one order of magnitude faster. © 2021 acm.

关键词： Decision making

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