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pith:PCW2GJ3L

pith:2026:PCW2GJ3LR7E6LBII6R75YZQVCW
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Multi-GPU Hybrid Particle-in-Cell Monte Carlo Simulations for Exascale Computing Systems

Ales Podolnik, Allen D. Malony, David Tskhakaya, Erwin Laure, Frank Jenko, Jakub Hromadka, Jeremy J. Williams, Jonah Ekelund, Jordy Trilaksono, Leon Kos, Luca Pennati, Sameer Shende, Stefan Costea, Stefano Markidis, Tilman Dannert, Yi Ju

A hybrid MPI+OpenMP implementation scales BIT1 Particle-in-Cell Monte Carlo simulations to 16,000 GPUs on exascale systems.

arxiv:2603.24508 v3 · 2026-03-25 · physics.plasm-ph · cs.DC · cs.PF

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Record completeness

1 Bitcoin timestamp
2 Internet Archive
3 Author claim open · sign in to claim
4 Citations open
5 Replications open
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Claims

C1strongest claim

Performance results on pre-exascale and exascale systems, including Frontier (OLCF-5) for up to 16,000 GPUs, demonstrate significant improvements in run time, scalability, and resource utilization for large-scale PIC MC simulations.

C2weakest assumption

The assumption that the described optimizations preserve the numerical accuracy and physical correctness of the original BIT1 simulation while delivering the claimed performance gains, which is not demonstrated or quantified in the provided abstract.

C3one line summary

A new multi-GPU hybrid MPI+OpenMP version of the BIT1 PIC MC code achieves scalable performance and improved resource utilization on up to 16,000 GPUs through OpenMP target tasks, persistent device memory, and standardized I/O.

Formal links

2 machine-checked theorem links

Cited by

1 paper in Pith

Receipt and verification
First computed 2026-06-09T02:07:25.022519Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

78ada3276b8fc9e58508f47fdc6615158fd6f3da730f0c5d0ec7a4d531247cec

Aliases

arxiv: 2603.24508 · arxiv_version: 2603.24508v3 · doi: 10.48550/arxiv.2603.24508 · pith_short_12: PCW2GJ3LR7E6 · pith_short_16: PCW2GJ3LR7E6LBII · pith_short_8: PCW2GJ3L
Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/PCW2GJ3LR7E6LBII6R75YZQVCW \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 78ada3276b8fc9e58508f47fdc6615158fd6f3da730f0c5d0ec7a4d531247cec
Canonical record JSON
{
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    "cross_cats_sorted": [
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      "cs.PF"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "physics.plasm-ph",
    "submitted_at": "2026-03-25T16:48:23Z",
    "title_canon_sha256": "6a2accee072054b275d826fbdc13c749d2982c9724877056ae8e30bcc7aa14a1"
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    "kind": "arxiv",
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}