HALO 1.0: A Hardware-agnostic Accelerator Orchestration Framework for Enabling Hardware-agnostic Programming with True Performance Portability for Heterogeneous HPC
Arizona State University（亚利桑那州立大学）
Michael Riera, Erfan Bank Tavakoli, Masudul Hassan Quraishi, Fengbo Ren
This paper presents HALO 1.0, an open-ended extensible multi-agent software framework that implements a set of proposed hardware-agnostic accelerator orchestration (HALO) principles. HALO implements a novel compute-centric message passing interface (C^2MPI) specification for enabling the performance portable execution of a hardware-agnostic host application across heterogeneous accelerators. The experiment results of evaluating eight widely used HPC subroutines based on Intel Xeon E5-2620 CPUs, Intel Arria 10 GX FPGAs, and NVIDIA GeForce RTX 2080 Ti GPUs show that HALO 1.0 allows for a unified control flow for host programs to run across all the computing devices with a consistently top performance portability score, which is up to five orders of magnitude higher than the OpenCL-based solution.
Containerisation demonstrates its efficiency in application deployment in cloud computing. Containers can encapsulate complex programs with their dependencies in isolated environments, hence are being adopted in HPC clusters. HPC workload managers lack micro-services support and deeply integrated container management, as opposed to container orchestrators (e.g. Kubernetes). We introduce Torque-Operator (a plugin) which serves as a bridge between HPC workload managers and container Orchestrators.
High-throughput structure-based screening of drug-like molecules has become a common tool in biomedical research. Recently, acceleration with graphics processing units (GPUs) has provided a large performance boost for molecular docking programs. Both cloud and high-performance computing (HPC) resources have been used for large screens with molecular docking programs; while NVIDIA GPUs have dominated cloud and HPC resources, new vendors such as AMD and Intel are now entering the field, creating the problem of software portability across different GPUs. Ideally, software productivity could be ma…
Containers improve the efficiency in application deployment and thus have been widely utilised on Cloud and lately in High Performance Computing (HPC) environments. Containers encapsulate complex programs with their dependencies in isolated environments making applications more compatible and portable. Often HPC systems have higher security levels compared to Cloud systems, which restrict users' ability to customise environments. Therefore, containers on HPC need to include a heavy package of libraries making their size relatively large. These libraries usually are specifically optimised for t…
Python has become the de facto language for scientific computing. Programming in Python is highly productive, mainly due to its rich science-oriented software ecosystem built around the NumPy module. As a result, the demand for Python support in High Performance Computing (HPC) has skyrocketed. However, the Python language itself does not necessarily offer high performance. In this work, we present a workflow that retains Python's high productivity while achieving portable performance across different architectures. The workflow's key features are HPC-oriented language extensions and a set of …
High-performance computing (HPC) is a major driver accelerating scientific research and discovery, from quantum simulations to medical therapeutics. While the increasing availability of HPC resources is in many cases pivotal to successful science, even the largest collaborations lack the computational expertise required for maximal exploitation of current hardware capabilities. The need to maintain multiple platform-specific codebases further complicates matters, potentially adding constraints on machines that can be utilized. Fortunately, numerous programming models are under development that…
The Julia programming language has evolved into a modern alternative to fill existing gaps in scientific computing and data science applications. Julia leverages a unified and coordinated single-language and ecosystem paradigm and has a proven track record of achieving high performance without sacrificing user productivity. These aspects make Julia a viable alternative to high-performance computing's (HPC's) existing and increasingly costly many-body workflow composition strategy in which traditional HPC languages (e.g., Fortran, C, C++) are used for simulations, and higher-level languages (e.…
Modern HPC systems are built with innovative system architectures and novel programming models to further push the speed limit of computing. The increased complexity poses challenges for performance portability and performance evaluation. The Standard Performance Evaluation Corporation -SPEC has a long history of producing industry standard benchmarks for modern computer systems. SPEC is a newly released SPEChpc 2021 benchmark suites, developed by the High Performance Group, are a bold attempt to provide a fair and objective benchmarking tool designed for state of the art HPC systems. With the…
Over recent years heterogeneous systems have become more prevalent across HPC systems, with over 100 supercomputers in the TOP500 incorporating GPUs or other accelerators. These hardware platforms have different performance characteristics and optimization requirements. In order to make the most of multiple accelerators a developer has to provide implementations of their algorithms tuned for each device. Hardware vendors provide libraries targeting their devices specifically, which provide good performance but frequently have different API designs, hampering portability. The SYCL programming m…
Building and deploying software on high-end computing systems is a challenging task. High performance applications have to reliably run across multiple platforms and environments, and make use of site-specific resources while resolving complicated software-stack dependencies. Containers are a type of lightweight virtualization technology that attempt to solve this problem by packaging applications and their environments into standard units of software that are: portable, easy to build and deploy, have a small footprint, and low runtime overhead. In this work we present an extension to the cont…
An increasingly large number of HPC systems rely on heterogeneous architectures combining traditional multi-core CPUs with power efficient accelerators. Designing efficient applications for these systems has been troublesome in the past as accelerators could usually be programmed using specific programming languages threatening maintainability, portability and correctness. Several new programming environments try to tackle this problem. Among them, OpenACC offers a high-level approach based on compiler directive clauses to mark regions of existing C, C++ or Fortran codes to run on accelerators…
Porting and Benchmarking Chapel on Emerging RISC-V Hardware: an HPC Viability Study
Sandia National Laboratories（桑迪亚国家实验室）；Tenstorrent；Tactical Computing Labs（战术计算实验室）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Hewlett Packard Enterprise（惠普企业）；FiscalNote
The world of HPC systems is changing to a more complicated system because the performance improvement of processors has been slowed down. One of the promising approaches is Domain-Specific Language(DSL), which provides a productive environment to create a high-efficient program without pain. However, existing DSL platforms themselves often lack portability and cost DSL developers great effort. To solve this issue, we propose an Aspect-Oriented Programming(AOP) based DSL constructing platform, enabling developers to build a DSL platform by combining Aspect modules. Aspect modules manage abstrac…
In the last fifteen years, the high performance computing (HPC) community has claimed for parallel programming environments that reconciles generality, higher level of abstraction, portability, and efficiency for distributed-memory parallel computing platforms. The Hash component model appears as an alternative for addressing HPC community claims for fitting these requirements. This paper presents foundations that will enable a parallel programming environment based on the Hash model to address the problems of "debugging", performance evaluation and verification of formal properties of paralle…
The application of high-performance computing (HPC) processes, tools, and technologies to Controlled Unclassified Information (CUI) creates both opportunities and challenges. Building on our experiences developing, deploying, and managing the Research Environment for Encumbered Data (REED) hosted by AWS GovCloud, Research Computing at Purdue University has recently deployed Weber, our locally-sited HPC solution for the storage and analysis of CUI data. Weber presents our customer base with advances in data access, portability, and usability at a low, stable cost while reducing administrative o…
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The analysis of next-generation sequencing (NGS) data requires complex computational workflows consisting of dozens of autonomously developed yet interdependent processing steps. Whenever large amounts of data need to be processed, these workflows must be executed on a parallel and/or distributed systems to ensure reasonable runtime. Porting a workflow developed for a particular system on a particular hardware infrastructure to another system or to another infrastructure is non-trivial, which poses a major impediment to the scientific necessities of workflow reproducibility and workflow reusab…
