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5 August 2026

  • 14:4014:40, 5 August 2026 IntelMPI (hist | edit) [12,183 bytes] Honfi001 (talk | contribs) (Created page with "'''Intel MPI''' is the other MPI implementation available on Anunna, alongside OpenMPI. It arrives as part of the Intel toolchain, together with the Intel compilers and the Intel Math Kernel Library (MKL). Everything general about MPI — that it runs one program as many cooperating processes, that it has to be built into the program, and when it is the right tool at all — is covered on Multi-Process Workflows. This page is the Intel-sp...")
  • 13:4313:43, 5 August 2026 OpenMPI (hist | edit) [14,775 bytes] Honfi001 (talk | contribs) (Created page with "'''OpenMPI''' is the MPI library Anunna is built around. MPI — the Message Passing Interface — is how a single program runs as many cooperating processes at once, each with its own private memory, passing messages between themselves as the calculation proceeds. It is what lets one job use more than one node. This page is the practical side: which OpenMPI modules exist here, how to launch a job on one node and across several, and how to choose the network transport,...")
  • 09:5409:54, 5 August 2026 Workflows/Multi-Process (hist | edit) [11,748 bytes] Honfi001 (talk | contribs) (Created page with "Multi-threaded work gave one process many arms, and then ran into a wall: threads share memory, and shared memory stops at the edge of the machine. This page is how a single calculation gets past that wall — not by growing one process, but by running '''many separate processes''', each with its own private memory, that send messages to each other as the calculation proceeds. The standard way to do that is '''MPI''', the Message Passing Inte...")
  • 07:3107:31, 5 August 2026 Workflows/Embarassinly-Parallel (hist | edit) [11,729 bytes] Honfi001 (talk | contribs) (Created page with "This tutorial is a gentle, hands-on introduction to '''parallel computing on Anunna'''. Instead of a heavy scientific code, it uses one friendly problem — estimating the number π by throwing random darts — and runs the ''same'' calculation across every kind of parallelism the cluster offers: a single core, many threads, many processes, multiple nodes, and a GPU. Because the problem stays the same, you can see exactly what each step up the ladder buys you. You do no...")

28 July 2026

  • 19:1519:15, 28 July 2026 Workflows/Multi-threaded (hist | edit) [7,827 bytes] Honfi001 (talk | contribs) (Created page with "Serial ran one program on one core. This page takes that same job and lets the program spread its work across '''many''' cores on the '''same''' machine — still one process, but now with several ''threads'' inside it, all sharing the same memory. It is the gentlest real step up the ladder, because for most tools someone else already did the hard part: the program already knows how to use extra cores. Your job is only to give it some, and...")

27 July 2026

  • 10:3510:35, 27 July 2026 Workflows/Serial (hist | edit) [5,636 bytes] Honfi001 (talk | contribs) (Created page with "Every other page in this section describes a way of splitting work across many cores. This one describes ''not'' doing that — one program, running on a single core, from start to finish. It is the baseline, and, to be honest up front, it is not a parallel workflow at all. It earns a page anyway, for two reasons. Most software you did not ''specifically'' choose for its parallelism runs this way, so this is where many jobs on Anunna may actually belong. And every othe...") originally created as "Workflows/Serial-Workflows"

23 July 2026

  • 15:1815:18, 23 July 2026 Workflows/Parallel-Computing (hist | edit) [7,702 bytes] Honfi001 (talk | contribs) (Created page with "Your analysis runs. It just runs for too long, or it no longer fits on one machine. '''Parallel computing''' is the answer to both, and this page is the map: what it actually means on Anunna, and which of the six workflow types below is yours. Each section is a short paragraph and a link. Read them, recognise your own problem in one of them, and follow it. If you would rather learn by doing, the π estimation tutorial walks the sa...") originally created as "Workflows/Paralle-Computing"

14 July 2026

24 June 2026

  • 08:4008:40, 24 June 2026 Tutorials/Parallelism-Estimating-Pi (hist | edit) [15,432 bytes] Honfi001 (talk | contribs) (Created page with "This tutorial is a gentle, hands-on introduction to '''parallel computing on Anunna'''. Instead of a heavy scientific code, it uses one friendly problem — estimating the number π by throwing random darts — and runs the ''same'' calculation across every kind of parallelism the cluster offers: a single core, many threads, many processes, multiple nodes, and a GPU. Because the problem stays the same, you can see exactly what each step up the ladder buys you. You do no...")

19 June 2026

18 June 2026

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