High‑Performance Computing Overview and Resource Guide

High‑Performance Computing (HPC) refers to computing systems that use many processors within a single machine or a cluster of machines to provide a unified computational resource, ranging from large clusters to specialized hardware.

The article references two sources, “High‑Performance Computing Knowledge Summary” and “OpenMP Compilation Principles and Implementation,” and offers download links for related technologies.

Most cluster‑based HPC systems employ high‑performance network interconnects such as InfiniBand or Myrinet, with simple bus topologies for basic setups and mesh networks for low latency in high‑performance environments.

HPC Hardware and Overall Architecture includes compute nodes, I/O nodes, login and management nodes, high‑speed networks, and storage systems.

Performance Metrics focus on FLOPS (floating‑point operations per second) as the primary measure of computational capability, with theoretical performance calculations for CPUs, GPUs, and MICs based on clock frequency, vector width, and instruction counts.

Benchmarking tools such as Linpack (using Gaussian elimination) and High‑Performance Linpack (HPL) are described for evaluating overall system performance and for TOP500/TOP100 rankings.

Node Types are distinguished between homogeneous nodes (identical CPU resources) and heterogeneous nodes (combinations like CPU+GPU, CPU+MIC, CPU+FPGA) that improve scalability and energy efficiency.

Common Parallel File Systems include PVFS, an open‑source parallel virtual file system for Linux clusters, and Lustre, a widely used parallel distributed file system for large clusters and supercomputers.

Cluster Management Functions cover monitoring, user management, network management, file management, power management, job submission and management, and graphical user interfaces, with examples of vendor‑specific solutions such as Inspur Cluster Engine, Sugon Gridview, HP Serviceguard, and IBM Platform Cluster Manager.

Job Scheduling Systems are primarily based on PBS (Portable Batch System), originally developed by NASA Ames, with variants including OpenPBS, PBS Pro, and Torque, offering features like ease of use, POSIX compliance, adaptability, and support for interactive and batch jobs.

Parallel Programming Models focus on MPI (Message Passing Interface), describing it as a library and standard for message‑based parallel programming, and listing major implementations: OpenMPI, Intel MPI, MPICH, and MVAPICH (including MVAPICH2), each with download links.

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