pith:ZM7Y2R4G
GPU Performance of an Entropy-Stable Discontinuous Galerkin Euler Solver with Non-Conservative Terms
Entropy-stable discontinuous Galerkin solver for Euler equations with buoyancy reaches nearly 70% of GPU peak performance.
arxiv:2605.16684 v1 · 2026-05-15 · math.NA · cs.NA
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Claims
On NVIDIA A100 hardware, the solver achieves nearly 70% of 64-bit floating-point peak performance for the most computationally expensive kernel (volume terms) ... factor of 10× faster and better than 13× more energy efficient than the CPU code ... extend symmetry-based flux savings to the non-symmetric gravity term, preserving nearly the full factor-of-two speedup.
The sequence of GPU-specific modifications (reduced complex operations, lower memory traffic, load balancing) preserves both the formal entropy stability and the numerical accuracy of the original discontinuous Galerkin scheme when non-conservative buoyancy terms are present.
GPU port of entropy-stable DG Euler solver with non-conservative buoyancy terms reaches nearly 70% of 64-bit peak on A100 volume kernels, delivers 10x speedup and 13x better energy efficiency versus CPU, and preserves symmetry-based flux savings.
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| First computed | 2026-05-20T00:02:36.495353Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
cb3f8d4786cf66b735cfaab4ff34fae838d333da9fec14084edf71e5c7162510
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZM7Y2R4GZ5TLONOPVK2P6NH25A \
| 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: cb3f8d4786cf66b735cfaab4ff34fae838d333da9fec14084edf71e5c7162510
Canonical record JSON
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