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pith:DG2UPY27

pith:2026:DG2UPY27XQIOX6MUAXTYMXF6FA

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Comparing the Performance of Heterogeneous Conjugate Gradient and Cholesky Solvers on Various Hardware Using SYCL

Alexander Strack, Dirk Pfl\"uger, Tim Th\"uring

Heterogeneous CPU-GPU implementations of CG and Cholesky solvers run up to 32 percent faster than GPU-only versions for large matrices.

arxiv:2605.13209 v1 · 2026-05-13 · cs.DC · cs.PF

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Claims

C1strongest claim

For large matrices, our heterogeneous implementation is up to 32 percent faster for the CG method and up to 29 percent faster for the Cholesky decomposition compared to the corresponding GPU-only implementations. In addition, for large matrices, our heterogeneous implementation of the Cholesky decomposition can achieve at least 12 percent faster runtimes across several systems with GPUs from NVIDIA, AMD, and Intel.

C2weakest assumption

That the overhead of data movement and synchronization between CPU and GPU remains low enough for the heterogeneous schedule to outperform a well-tuned GPU-only kernel on the tested matrix sizes and hardware configurations.

C3one line summary

Heterogeneous SYCL-based CG and Cholesky solvers deliver up to 32% and 29% faster runtimes than GPU-only versions for large matrices across multiple GPU vendors.

References

41 extracted · 41 resolved · 1 Pith anchors

[1] Ahmad Abdelfattah, Natalie Beams, Robert Carson, Pieter Ghysels, Tzanio Kolev, Thomas Stitt, Arturo Vargas, Stanimire Tomov, and Jack Dongarra. 2024. MAGMA: Enabling exascale performance with accelera 2024 · doi:10.1177/10943420241261960

[2] Pedro Alonso, Manuel F. Dolz, Francisco D. Igual, Rafael Mayo, and Enrique S. Quintana-Ortí. 2012. Reducing Energy Consumption of Dense Linear Alge- bra Operations on Hybrid CPU-GPU Platforms. In2012 2012 · doi:10.1109/ispa.2012.16

[3] Aksel Alpay and Vincent Heuveline. 2020. SYCL beyond OpenCL: The archi- tecture, current state and future direction of hipSYCL. InProceedings of the International Workshop on OpenCL (IWOCL ’20). Assoc 2020 · doi:10.1145/3388333.3388658

[4] AMD. 2022. AMD INSTINCT™MI210 ACCELERATOR. https: //www.amd.com/content/dam/amd/en/documents/instinct-business- docs/product-briefs/instinct-mi210-brochure.pdf 2022

[5] AMD. 2023. AMD EPYC™9004 SERIES PROCESSORS. https://www.amd.com/ content/dam/amd/en/documents/epyc-business-docs/datasheets/amd-epyc- 9004-series-processors-datasheet.pdf 2023

Receipt and verification

First computed	2026-05-18T03:08:48.561514Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

19b547e35fbc10ebf99405e7865cbe28353af6cf8a183e72d3c36d89f4ee8d4b

Aliases

arxiv: 2605.13209 · arxiv_version: 2605.13209v1 · doi: 10.48550/arxiv.2605.13209 · pith_short_12: DG2UPY27XQIO · pith_short_16: DG2UPY27XQIOX6MU · pith_short_8: DG2UPY27

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/DG2UPY27XQIOX6MUAXTYMXF6FA \
  | 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: 19b547e35fbc10ebf99405e7865cbe28353af6cf8a183e72d3c36d89f4ee8d4b

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.DC",
    "submitted_at": "2026-05-13T08:58:53Z",
    "title_canon_sha256": "0bae5c5087a6572d16a9701001eb43c7c952fb0b28e08ba828b989642e7f62b1"
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