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REVIEW 3 major objections 6 minor 65 references

Scalable High-Fidelity Macromolecular Docking for GPU-Accelerated Supercomputers

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read SparkleDock claims it keeps LightDock's 51/55 docking success rate while cutting runtime by 9.7x on A100, 18.9x on H100, and over 100x at scale.

desk verdict Solid systems paper with a real accuracy-evidence gap: the speedups look real, but the 51/55 success rate is inherited from LightDock, not measured for SparkleDock. read the letter →

arxiv 2608.07078 v1 pith:M23KQNXY submitted 2026-08-07 cs.DC cs.AI

Xiangyu Meng , Peng Chen , Mingzhen Li , Jianmin Wang , Sen Wang , Guangming Tan , Weile Jia , Mohamed Wahib
show 2 more authors
Tao Luo Xun Wang
This is my paper · ORCID
classification cs.DCcs.AI
keywords flexiblemacromoleculardockingglowwormswarmoptimizationGPUaccelerationtensorcoreunitsDFIREenergyscoringloadbalancingstrongscalingprotein-protein
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

SparkleDock is a GPU rewrite of the LightDock flexible docking algorithm. The paper claims that by exposing parallelism at individual glowworm agents, reformulating the dominant DFIRE energy scoring as a Tensor-Core matrix product, and using a performance model to divide work across GPUs, it preserves the same search and the same docking success rate while accelerating runtime by 9.7x on an A100, 18.9x on an H100, and by more than two orders of magnitude at 512-GPU scale. The upshot is that flexible docking, which normally takes hours per complex, becomes a seconds-scale screening tool on large GPU clusters. The accuracy claim is demonstrated on nine of the 55 BM5.2 complexes, inheriting LightDock's published 51/55 success rate.

What carries the argument

The load-bearing mechanism is the Gram-matrix reformulation of the DFIRE pairwise distance computation: Eq. (2) turns the quadratic all-pairs distance grid into one matrix multiplication plus row and column squared norms. Around it, the paper builds agent-level loop reordering that maps the swarm and agent loops onto a 3D CUDA grid; an mma.m8n8k4 FP64 tensor-core kernel with warp-shuffle register remapping to compute the norms inside the fragment registers; a cp.async multi-stage pipeline; and a performance model, $T_{total}$ and $M_{em total}$, that drives static MPI load balancing and automatic out-of-core chunk division.

What would settle it

Run SparkleDock on all 55 BM5.2 complexes with the same settings and compare the Top-10 success rate against LightDock's reported 51/55; a success rate materially below 51/55, or a divergence in the final pose distributions on the untested 46 complexes, would falsify the high-fidelity claim even if the speedups hold. A cheaper check would compare the per-step glowworm agent states after a fixed random seed between LightDock and SparkleDock to see whether the trajectories agree exactly.

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Extended reading notes

Core claim

The central discovery is that the performance bottleneck of glowworm-swarm docking is algorithmically removable without changing what the swarm computes. SparkleDock unrolls the swarm and agent loops into fine-grained agent-level tasks, then rewrites the pairwise distance and DFIRE energy evaluation using the identity $D_{x,y}^2 = \sum_k A_{x,k}^2 + \sum_k B_{y,k}^2 - 2\sum_k A_{x,k}B_{y,k}$, so the expensive cross term becomes a tall-skinny matrix multiply $A B^T$ that FP64 tensor cores can execute while CUDA cores compute the squared norms. Register remapping via warp shuffles and cp.async pipelining keep the operation on-chip. The paper argues that because all arithmetic remains in FP64 and the pose update rules are unchanged, the glowworm trajectories are identical to LightDock's, so SparkleDock reproduces LightDock's docking accuracy; its experiments on nine complexes show identical success rates.

Load-bearing premise

SparkleDock's accuracy claim depends on the assumption that its loop reordering and energy reformulation leave the glowworm agents' search trajectories identical to LightDock's, so that success rates measured on LightDock transfer to SparkleDock; the paper tests accuracy on only 9 of the 55 benchmark complexes.

Editorial extensions

If this is right

  • If the equivalence claim holds, any docking job run on LightDock can be run on SparkleDock with the same expected accuracy but at 9.7x and 18.9x single-GPU speedups on A100 and H100.
  • At 512 GPUs, the roughly two-orders-of-magnitude additional scaling means a workload that took hours finishes in seconds, making flexible docking a practical pre-filter for large-scale virtual screening.
  • The TCU reformulation of pairwise distance computation gives a template for turning other irregular all-pairs distance workloads into structured matrix multiplies.
  • The performance-model-driven chunk division lets one large complex, such as 4GAM with hundreds of gigabytes of pose data, run within a limited GPU memory instead of failing with out-of-memory errors.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the trajectory-equivalence assumption is confirmed across all 55 benchmark complexes, the speedups can be read as a pure implementation gain: LightDock's known accuracy is preserved rather than traded off for speed.
  • The same Gram-matrix distance reformulation could be applied to other distance-based scoring functions beyond DFIRE, potentially extending the tensor-core speedup to molecular dynamics contact maps and pairwise-kernel machine learning workloads.
  • A risk the paper leaves implicit is that FP64 tensor-core rounding, while nominally the same precision, could change last-bit distances and therefore binning boundaries, occasionally rerouting an agent; this is testable by comparing the final pose sets at high rank thresholds.
  • The strong-scaling results show saturation on small workloads like 2VXT, so the practical sweet spot is large complexes or large screening batches rather than single small docking jobs.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper presents SparkleDock, a GPU-accelerated reimplementation of the LightDock flexible macromolecular docking algorithm. The authors redesign the GSO simulation loop to expose agent-level parallelism, reformulate the dominant DFIRE pairwise-distance energy computation as a Tensor Core-compatible matrix product via a Gram-matrix expansion (Eq. 2), and add a performance-model-driven load-balancing and out-of-core chunking scheme for multi-GPU execution. The paper reports 9.7x/18.9x single-GPU speedups over LightDock-Rust on A100/H100, 183.1x strong scaling speedup on 512 A100 GPUs for 4GAM, and identical Top-N success rates to LightDock on nine BM5.2 complexes. The central claim is that SparkleDock preserves LightDock's docking accuracy while achieving the reported performance gains.

Significance. If the accuracy claim were fully supported, this would be a substantial contribution: it would reduce flexible protein-protein docking from an hour-scale batch job to a second-scale screening tool on GPU supercomputers, and the TCU reformulation of irregular pairwise distance computations is a transferable systems contribution. The performance measurements are the strongest part of the paper: the controlled CUDA-core comparison isolates the TCU benefit (1.99x on A100, 2.43x on H100), the roofline and breakdown analyses are informative, and the performance model is validated with a 12.55% MAPE on representative complexes. The authors also provide explicit scaling and load-balance measurements up to 512 GPUs. However, the 'high-fidelity' component of the central claim rests on an unmeasured extrapolation: Table I asserts a 51/55 success rate for SparkleDock, but Section IV-F evaluates only nine complexes, and the reported success rates are identical to LightDock's. The paper needs either a full BM5.2 run for SparkleDock or a clear, evidence-backed argument that the transformations preserve the search trajectory exactly.

major comments (3)
  1. [Section IV-F and Table I] Table I lists 'SparkleDock (ours)' with 51/55 (92.7%) on BM5.2, identical to the LightDock rows, but Section IV-F evaluates docking accuracy on only nine selected complexes (Tab. III), reporting 88.9% Top-10 success in Fig. 10. No full 55-complex run of SparkleDock is reported anywhere in the paper. The headline accuracy figure therefore appears to be inherited from LightDock rather than measured for SparkleDock. This is load-bearing for the 'high-fidelity' claim in the abstract and conclusion. The authors should either run the full BM5.2 benchmark (or provide per-complex SparkleDock results) or explicitly state that the 51/55 figure is a LightDock value carried over, which would require rewriting the abstract, Table I, and the accuracy discussion.
  2. [Section III-B, Eq. (2), and Algorithm 1 line 11] The paper argues that the Gram-matrix reformulation of the pairwise distance computation preserves semantics, but Eq. (2) computes distances as ||A||^2 + ||B||^2 - 2A·B, which subtracts large, nearly equal floating-point values. In FP64 this can still produce rounding errors that change the bin index assigned by DistBinning (Alg. 1, line 11), and a single bin change can alter the energy score, the neighbor list, and subsequent GSO movement. The use of FP64 is not by itself a proof of numerical equivalence. The authors should provide a direct comparison---for example, the fraction of distance matrix entries that land in the same DFIRE bin under Eq. (1) versus Eq. (2) on the nine test complexes, or a comparison of final poses and energy ranks---to demonstrate that the reformulation is indistinguishable from the original scoring computation.
  3. [Section IV-F, final paragraph] The statement that SparkleDock's success rates are 'identical to those obtained by the LightDock implementation' is only supported for the nine selected complexes, yet the conclusion generalizes to 'preserves the docking accuracy of LightDock.' Even on those nine complexes, the Top-1 success rate is 55.6% (5/9) and the Top-10 is 88.9% (8/9), so the 'identical' claim is not uniformly true across rank thresholds. The authors should qualify the equivalence claim to the measured set and thresholds, or supply per-complex success/failure data for both methods so the reader can judge whether the selected nine are representative of the full benchmark.
minor comments (6)
  1. [Table IV] The rows labeled 'Chunk Division ✔ - -' are ambiguous: it is unclear which complexes required automatic out-of-core chunk division on A100 and H100, and the dash symbols are not explained in the caption or text.
  2. [Section IV-F and Fig. 10] The success rate bars in Fig. 10 appear to be computed from only nine complexes; the figure should state the number of complexes and ideally show per-complex counts (e.g., 5/9, 8/9, 8/9, ...) rather than percentages alone, to prevent overinterpretation.
  3. [Section IV-H] The phrase 'effective strong scaling' is not standard; the paper should report parallel efficiency or the achieved speedup relative to the single-GPU baseline for each complex, especially for 2VXT where the speedup plateaus at 32.1x on 256 GPUs.
  4. [Section III-C] The performance model parameters T_H[ener/nei/move/pose] are described as obtained through microbenchmarks, but the paper does not list the measured values; including them (or a reference to a reproducibility appendix) would strengthen the model's credibility.
  5. [General] No code or artifact availability statement is provided. For a systems paper whose central claim is that a specific implementation preserves another algorithm's accuracy, releasing the source code and benchmark scripts is important for independent verification.
  6. [Abstract and Conclusion] The phrase 'high-fidelity' in the abstract and conclusion should be qualified in light of the limited accuracy evaluation; a phrase such as 'preserves accuracy on the evaluated BM5.2 subset' would be more precise until the full benchmark is run.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the speedup claims are direct measurements and the Eq. 2 reformulation is an algebraic identity; the flagged concern is an accuracy figure inherited from LightDock rather than measured over the full benchmark, which is a reporting gap, not a circular derivation.

full rationale

The paper's central speedup claims are end-to-end measurements against the LightDock-Rust CPU baseline (Table IV), so they are externally validated and do not reduce to any fitted parameter. The TCU reformulation in Eq. 2 is a mathematical identity of Eq. 1, and the paper explicitly evaluates in FP64 to preserve numerical semantics, so it is not a fitted input. The performance model (Eqs. 3-11) uses kernel throughputs obtained by microbenchmarks, but it is validated against measured runtimes (Table V, MAPE 12.55%) and is used only for load balancing and chunk division, not to manufacture speedups. The one notable issue is an unsupported accuracy extrapolation, not circularity: Section IV-F evaluates only nine BM5.2 complexes, and Fig. 10 reports success rates identical to LightDock on those nine, yet Table I lists SparkleDock as 51/55 (92.7%), the same number as LightDock's full-benchmark result. That full-benchmark accuracy is apparently inherited by an algorithmic-equivalence assumption rather than measured for SparkleDock; this should be weighed as a correctness or completeness risk, but it does not make the derivation circular because the speedup and reformulation results stand on independent measurements.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

No free parameters are fitted to the central claims. The performance model's throughput values are measured hardware parameters, not fitted to the endpoint. The listed axioms are the implicit assumptions under which the accuracy-preservation and scalability claims rest.

assumptions (5)
  • domain assumption SparkleDock's GSO search is algorithmically equivalent to LightDock's, so docking accuracy is inherited.
    This assumption underlies the 'identical success rates' claim in Section IV-F and the 51/55 entry in Table I. The paper tests 9 of 55 BM5.2 complexes; equivalence is assumed for the rest.
  • domain assumption FP64 arithmetic in the TCU reformulation (Eq. 2) preserves the DFIRE scoring semantics of LightDock.
    The decomposition of pairwise distances into Gram-matrix products and squared norms is exact; rounding-order differences are assumed not to change selected poses.
  • domain assumption GSO simulation dynamics are deterministic enough that loop reordering and kernel fusion do not alter the energy landscape.
    Used throughout Section III-A to justify reordering swarm and agent loops.
  • domain assumption The runtime components in Eq. 10 are additive and independent, and the hardware parameters TH_* and BW_pci are constant during execution.
    This is the foundation of the performance model in Section III-C used for load balancing and chunk division.
  • standard math Algebraic identity D^2 = ||A||^2 + ||B||^2 - 2 A B^T holds for Euclidean distances.
    Used in Eq. 2 to enable TCU execution.

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Pith. "Pith review of Scalable High-Fidelity Macromolecular Docking for GPU-Accelerated Supercomputers." pith.science (2026). https://pith.science/paper/M23KQNXY

@misc{pith2026260807078,
  author       = {Pith},
  title        = {Pith review of: Scalable High-Fidelity Macromolecular Docking for GPU-Accelerated Supercomputers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M23KQNXY}},
  note         = {Machine review of arXiv:2608.07078}
}
abstract

Flexible macromolecular docking offers high-fidelity predictions of biomolecular interactions, but remains prohibitively expensive at scale. Among existing approaches, LightDock leverages Glowworm Swarm Optimization (GSO) for accuracy, yet suffers from limited parallelism, irregular computation, and severe load imbalance, preventing efficient execution on GPU supercomputers. We present SparkleDock, a scalable GSO-based docking framework enabling near-real-time flexible docking. We redesign GSO to expose massive fine-grained parallelism at the glowworm-agent level, and restructure the dominant energy scoring computation into a Tensor Core-compatible formulation, enabling efficient execution of irregular pairwise interactions through structured matrix operations. We further introduce a performance-model-driven scheduling for load balancing and out-of-core scaling across GPUs. SparkleDock achieves 9.7 $\times$ and 18.9 $\times$ speedups over LightDock on single A100 and H100 GPU, and delivers over two orders of magnitude acceleration at scale. On 512 GPUs, it reduces docking time from hours to seconds, enabling large-scale, high-fidelity virtual screening previously impractical with flexible docking.

Figures

Figures reproduced from arXiv: 2608.07078 by the authors.

Figure 1
Figure 1. Overview of the SparkleDock framework. SparkleDock consists of molecule preparation, swarm initialization, agent initialization, and simulation (bottleneck). To accelerate the simulation, we devise agent-level parallelization, TCU accelerated energy score calculation, and performance model-guided scaling optimization. cost of significantly increased computation. Methods such as SwarmDOCK [28], HADDOCK [51], and Rose… view at source ↗
Figure 2
Figure 2. Implementation of the TCU-accelerated calc dfire kernel. (a) TCU-aware warp-level partition. (b) Thread-level distance calculation. (c) Accumulate the energy scores. simulation. As shown in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Overlapping the memory and calculation pipeline using [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figures from the paper (6 more)
Figure 5
Figure 5. Figure 5: Performance model guided out-of-core automatic chunk [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Roofline analysis of SparkleDock kernels at double precision on A100. 2VXT 3VLB 2A1A 1RKE 2GTP 4GAM 2X9A 4JCV 4LW4 2VXT 3VLB 2A1A 1RKE 2GTP 4GAM 2X9A 4JCV 4LW4 0 2 4 6 0 1 2 3 4 5 Perf. (TFLOP/s) Speedup TCU TCU+RR TCU+RR+PO TCU+RR (Speedup) TCU+RR+PO (Speedup) A100 H1…
Figure 7
Figure 7. Figure 7: Step-wise breakdown of the impact of different optimizations applied to the hotspot [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Performance improvement of TCU-accelerated [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]
Figure 9
Figure 9. Figure 9: SparkleDock’s prediction, run on A100 GPU. The magenta ligand is experimental (X-ray crystallography); cyan is the prediction. Top-1 Top-5 Top-10 Top-20 Top-50 Top-100 0 20 40 60 80 100 Success Rate (%) LightDock SparkleDock [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]
Figure 12
Figure 12. Figure 12: Load balance evaluation of MPI ranks (The green bars [PITH_FULL_IMAGE:figures/full_fig_p011_12.png]

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Works this paper leans on

65 extracted references · 48 canonical work pages

  1. [1]

    Protein–protein interaction networks: unraveling the wiring of molecular machines within the cell,

    J. De Las Rivas and C. Fontanillo, “Protein–protein interaction networks: unraveling the wiring of molecular machines within the cell,” Briefings in functional genomics, vol. 11, no. 6, pp. 489–496, 2012. [Online]. Available: https://doi.org/10.1093/bfgp/els036

    work page doi:10.1093/bfgp/els036 2012

  2. [2]

    A human protein-protein interaction network: a resource for annotating the proteome,

    U. Stelzl, U. Worm, M. Lalowski, C. Haenig, F. H. Brembeck, H. Goehler, M. Stroedicke, M. Zenkner, A. Schoenherr, S. Koeppen et al., “A human protein-protein interaction network: a resource for annotating the proteome,”Cell, vol. 122, no. 6, pp. 957–968, 2005. [Online]. Available: https://doi.org/10.1016/j.cell.2005.08.029

    work page doi:10.1016/j.cell.2005.08.029 2005

  3. [3]

    Exploring the conformational ensembles of protein–protein complex with transformer-based generative model,

    J. Wang, X. Wang, Y . Chu, C. Li, X. Li, X. Meng, Y . Fang, K. T. No, J. Mao, and X. Zeng, “Exploring the conformational ensembles of protein–protein complex with transformer-based generative model,” Journal of Chemical Theory and Computation, 2024. [Online]. Available: https://doi.org/10.1021/acs.jctc.4c00255

    work page doi:10.1021/acs.jctc.4c00255 2024

  4. [4]

    Structure-based cross-docking analysis of antibody–antigen interactions,

    K. P. Kilambi and J. J. Gray, “Structure-based cross-docking analysis of antibody–antigen interactions,”Scientific reports, vol. 7, no. 1, p. 8145,

    work page

  5. [5]

    Development of therapeutic antibodies for the treatment of diseases,

    R.-M. Lu, Y .-C. Hwang, I.-J. Liu, C.-C. Lee, H.-Z. Tsai, H.-J. Li, and H.-C. Wu, “Development of therapeutic antibodies for the treatment of diseases,”Journal of biomedical science, vol. 27, pp. 1–30, 2020. [Online]. Available: https://doi.org/10.1186/s12929-019-0592-z

    doi:10.1186/s12929-019-0592-z 2020

  6. [6]

    Integrative modeling of membrane-associated protein assemblies,

    J. Roel-Touris, B. Jim ´enez-Garc´ıa, and A. M. Bonvin, “Integrative modeling of membrane-associated protein assemblies,”Nature communications, vol. 11, no. 1, p. 6210, 2020. [Online]. Available: https://doi.org/10.1038/s41467-020-20076-5

    work page doi:10.1038/s41467-020-20076-5 2020

  7. [7]

    Understanding the challenges of protein flexibility in drug design,

    D. A. Antunes, D. Devaurs, and L. E. Kavraki, “Understanding the challenges of protein flexibility in drug design,”Expert opinion on drug discovery, vol. 10, no. 12, pp. 1301–1313, 2015. [Online]. Available: https://doi.org/10.1517/17460441.2015.1094458

    work page arXiv 2015

  8. [8]

    Protein structure-based drug design: from docking to molecular dynamics,

    P. ´Sled´z and A. Caflisch, “Protein structure-based drug design: from docking to molecular dynamics,”Current opinion in structural biology, vol. 48, pp. 93–102, 2018. [Online]. Available: https: //doi.org/10.1016/j.sbi.2017.10.010

    work page doi:10.1016/j.sbi.2017.10.010 2018

Show all 65 references
  1. [9]

    Lightdock: a new multi-scale approach to protein–protein docking,

    B. Jim ´enez-Garc´ıa, J. Roel-Touris, M. Romero-Durana, M. Vidal, D. Jim ´enez-Gonz´alez, and J. Fern ´andez-Recio, “Lightdock: a new multi-scale approach to protein–protein docking,”Bioinformatics, vol. 34, no. 1, pp. 49–55, 2018. [Online]. Available: https: //doi.org/10.1093...

    2018 doi

  2. [10]

    Lightdock goes information-driven,

    J. Roel-Touris, A. M. Bonvin, and B. Jim ´enez-Garc´ıa, “Lightdock goes information-driven,”Bioinformatics, vol. 36, no. 3, pp. 950–952, 2020. [Online]. Available: https://doi.org/10.1093/bioinformatics/btz642

    2020 doi

  3. [11]

    Uniprot: a hub for protein information,

    U. Consortium, “Uniprot: a hub for protein information,”Nucleic acids research, vol. 43, no. D1, pp. D204–D212, 2015. [Online]. Available: https://doi.org/10.1093/nar/gku989

    2015 doi

  4. [12]

    The improvement of glowworm swarm optimization for continuous optimization problems,

    B. Wu, C. Qian, W. Ni, and S. Fan, “The improvement of glowworm swarm optimization for continuous optimization problems,”Expert systems with applications, vol. 39, no. 7, pp. 6335–6342, 2012. [Online]. Available: https://doi.org/10.1016/j.eswa.2011.12.017

    2012 doi

  5. [13]

    Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions,

    K. Krishnanand and D. Ghose, “Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions,” Swarm intelligence, vol. 3, no. 2, pp. 87–124, 2009. [Online]. Available: https://doi.org/10.1007/s11721-008-0021-5

    2009 doi

  6. [14]

    Nvidia tensor core programmability, performance & precision,

    S. Markidis, S. W. Der Chien, E. Laure, I. B. Peng, and J. S. Vetter, “Nvidia tensor core programmability, performance & precision,” in 2018 IEEE international parallel and distributed processing symposium workshops (IPDPSW). IEEE, 2018, pp. 522–531. [Online]. Available: https...

    2018

  7. [15]

    Piper: an fft-based protein docking program with pairwise potentials,

    D. Kozakov, R. Brenke, S. R. Comeau, and S. Vajda, “Piper: an fft-based protein docking program with pairwise potentials,”Proteins: Structure, Function, and Bioinformatics, vol. 65, no. 2, pp. 392–406,

  8. [16]

    Gpu acceleration of a production molecular docking code,

    B. Sukhwani and M. C. Herbordt, “Gpu acceleration of a production molecular docking code,” inProceedings of 2nd workshop on general purpose processing on graphics processing units, 2009, pp. 19–27. [Online]. Available: https://doi.org/10.1145/1513895.1513898

    2009

  9. [17]

    Gpu optimizations for a production molecular docking code,

    R. Landaverde and M. C. Herbordt, “Gpu optimizations for a production molecular docking code,” in2014 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2014, pp. 1–6. [Online]. Available: https://doi.org/10.1109/HPEC.2014.7040981

    2014

  10. [18]

    pydock: Electrostatics and desolvation for effective scoring of rigid-body protein– protein docking,

    T. M.-K. Cheng, T. L. Blundell, and J. Fernandez-Recio, “pydock: Electrostatics and desolvation for effective scoring of rigid-body protein– protein docking,”Proteins: Structure, Function, and Bioinformatics, vol. 68, no. 2, pp. 503–515, 2007. [Online]. Available: https: //doi...

    2007 doi

  11. [19]

    Megadock: an all-to-all protein-protein interaction prediction system using tertiary structure data,

    M. Ohue, Y . Matsuzaki, N. Uchikoga, T. Ishida, and Y . Akiyama, “Megadock: an all-to-all protein-protein interaction prediction system using tertiary structure data,”Protein and peptide letters, vol. 21, no. 8, pp. 766–778, 2014. [Online]. Available: https://doi.org/10.2174/ ...

    2014

  12. [20]

    Accelerating protein docking in zdock using an advanced 3d convolution library,

    B. G. Pierce, Y . Hourai, and Z. Weng, “Accelerating protein docking in zdock using an advanced 3d convolution library,” PloS one, vol. 6, no. 9, p. e24657, 2011. [Online]. Available: https://doi.org/10.1371/journal.pone.0024657

    2011 doi

  13. [21]

    M-zdock: a grid-based approach for c n symmetric multimer docking,

    B. Pierce, W. Tong, and Z. Weng, “M-zdock: a grid-based approach for c n symmetric multimer docking,”Bioinformatics, vol. 21, no. 8, pp. 1472–1478, 2005. [Online]. Available: https: //doi.org/10.1093/bioinformatics/bti229

    2005 doi

  14. [22]

    Integrating statistical pair potentials into protein complex prediction,

    J. Mintseris, B. Pierce, K. Wiehe, R. Anderson, R. Chen, and Z. Weng, “Integrating statistical pair potentials into protein complex prediction,” Proteins: Structure, Function, and Bioinformatics, vol. 69, no. 3, pp. 511–520, 2007. [Online]. Available: https://doi.org/10.1002/p...

    2007 doi

  15. [23]

    Zdock: an initial-stage protein- docking algorithm,

    R. Chen, L. Li, and Z. Weng, “Zdock: an initial-stage protein- docking algorithm,”Proteins: Structure, Function, and Bioinformatics, vol. 52, no. 1, pp. 80–87, 2003. [Online]. Available: https: //doi.org/10.1002/prot.10389

    2003 doi

  16. [24]

    The haddock web server for data-driven biomolecular docking,

    S. J. De Vries, M. Van Dijk, and A. M. Bonvin, “The haddock web server for data-driven biomolecular docking,”Nature protocols, vol. 5, no. 5, pp. 883–897, 2010. [Online]. Available: https: //doi.org/10.1038/nprot.2010.32

    2010 doi

  17. [25]

    The rosettadock server for local protein– protein docking,

    S. Lyskov and J. J. Gray, “The rosettadock server for local protein– protein docking,”Nucleic acids research, vol. 36, no. suppl 2, pp. W233–W238, 2008. [Online]. Available: https://doi.org/10.1093/nar/ gkn216

    2008 doi

  18. [26]

    Efficient flexible backbone protein–protein docking for challenging targets,

    N. A. Marze, S. S. Roy Burman, W. Sheffler, and J. J. Gray, “Efficient flexible backbone protein–protein docking for challenging targets,”Bioinformatics, vol. 34, no. 20, pp. 3461–3469, 2018. [Online]. Available: https://doi.org/10.1093/bioinformatics/bty355

    2018 doi

  19. [27]

    Swarmdock: a server for flexible protein–protein docking,

    M. Torchala, I. H. Moal, R. A. Chaleil, J. Fernandez-Recio, and P. A. Bates, “Swarmdock: a server for flexible protein–protein docking,” Bioinformatics, vol. 29, no. 6, pp. 807–809, 2013. [Online]. Available: https://doi.org/10.1093/bioinformatics/btt038

    2013 doi

  20. [28]

    Swarmdock and the use of normal modes in protein-protein docking,

    I. H. Moal and P. A. Bates, “Swarmdock and the use of normal modes in protein-protein docking,”International journal of molecular sciences, vol. 11, no. 10, pp. 3623–3648, 2010. [Online]. Available: https://doi.org/10.3390/ijms11103623

    2010 doi

  21. [29]

    Freesasa: An open source c library for solvent accessible surface area calculations,

    S. Mitternacht, “Freesasa: An open source c library for solvent accessible surface area calculations,”F1000Research, vol. 5, p. 189, 2016. [Online]. Available: https://doi.org/10.12688/f1000research.7931.1

    2016 doi

  22. [30]

    Animating rotation with quaternion curves,

    K. Shoemake, “Animating rotation with quaternion curves,” in Proceedings of the 12th annual conference on Computer graphics and interactive techniques, 1985, pp. 245–254. [Online]. Available: https://doi.org/10.1145/325165.325242

    1985

  23. [31]

    Dynamics of proteins predicted by molecular dynamics simulations and analytical approaches: Application toα-amylase inhibitor,

    P. Doruker, A. R. Atilgan, and I. Bahar, “Dynamics of proteins predicted by molecular dynamics simulations and analytical approaches: Application toα-amylase inhibitor,”Proteins: Structure, Function, and Bioinformatics, vol. 40, no. 3, pp. 512–524, 2000. [Online]. Available: h...

    doi

  24. [32]

    Anisotropy of fluctuation dynamics of proteins with an elastic network model,

    A. R. Atilgan, S. Durell, R. L. Jernigan, M. C. Demirel, O. Keskin, and I. Bahar, “Anisotropy of fluctuation dynamics of proteins with an elastic network model,”Biophysical journal, vol. 80, no. 1, pp. 505–515, 2001. [Online]. Available: https://doi.org/10.1016/S0006-3495(01)76033-X

    2001 doi

  25. [33]

    Ab initio folding of terminal segments with secondary structures reveals the fine difference between two closely related all-atom statistical energy functions,

    Y . Yang and Y . Zhou, “Ab initio folding of terminal segments with secondary structures reveals the fine difference between two closely related all-atom statistical energy functions,”Protein science, vol. 17, no. 7, pp. 1212–1219, 2008. [Online]. Available: https://doi.org/10...

    2008 doi

  26. [34]

    Quaternion interpolation with extra spins,

    J. Morrison, “Quaternion interpolation with extra spins,” inGraphics Gems III (IBM Version). Elsevier, 1992, pp. 96–97. [Online]. Available: https://doi.org/10.1016/B978-0-08-050755-2.50031-2

    1992 doi

  27. [35]

    Prody: protein dynamics inferred from theory and experiments,

    A. Bakan, L. M. Meireles, and I. Bahar, “Prody: protein dynamics inferred from theory and experiments,”Bioinformatics, vol. 27, no. 11, pp. 1575–1577, 2011. [Online]. Available: https://doi.org/10.1093/ bioinformatics/btr168

    2011

  28. [36]

    Nvidia a100 tensor core gpu: Performance and innovation,

    J. Choquette, W. Gandhi, O. Giroux, N. Stam, and R. Krashinsky, “Nvidia a100 tensor core gpu: Performance and innovation,”IEEE 12 Micro, vol. 41, no. 2, pp. 29–35, 2021. [Online]. Available: https://doi.org/10.1109/MM.2021.3061394

    2021

  29. [37]

    Accelerating reduction and scan using tensor core units,

    A. Dakkak, C. Li, J. Xiong, I. Gelado, and W.-m. Hwu, “Accelerating reduction and scan using tensor core units,” inProceedings of the ACM International Conference on Supercomputing, 2019, pp. 46–57. [Online]. Available: https://doi.org/10.1145/3330345.3331057

    2019

  30. [38]

    Convstencil: Transform stencil computation to matrix multiplication on tensor cores,

    Y . Chen, K. Li, Y . Wang, D. Bai, L. Wang, L. Ma, L. Yuan, Y . Zhang, T. Cao, and M. Yang, “Convstencil: Transform stencil computation to matrix multiplication on tensor cores,” inProceedings of the 29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Prog...

    2024

  31. [39]

    Toward accelerated stencil computation by adapting tensor core unit on gpu,

    X. Liu, Y . Liu, H. Yang, J. Liao, M. Li, Z. Luan, and D. Qian, “Toward accelerated stencil computation by adapting tensor core unit on gpu,” inProceedings of the 36th ACM International Conference on Supercomputing, 2022, pp. 1–12. [Online]. Available: https://doi.org/10.1145/...

    2022

  32. [40]

    Berrybees: Breadth first search by bit-tensor- cores,

    Y . Niu and M. Casas, “Berrybees: Breadth first search by bit-tensor- cores,” inProceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, 2025, pp. 339–354. [Online]. Available: https://doi.org/10.1145/3710848.3710859

    2025

  33. [41]

    tcfft: A fast half-precision fft library for nvidia tensor cores,

    B. Li, S. Cheng, and J. Lin, “tcfft: A fast half-precision fft library for nvidia tensor cores,” in2021 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2021, pp. 1–11. [Online]. Available: https://doi.org/10.1109/Cluster48925.2021.00035

    2021

  34. [42]

    Real-time high-resolution x-ray computed tomography,

    D. Wu, P. Chen, X. Wang, I. Lyngaas, T. Miyajima, T. Endo, S. Matsuoka, and M. Wahib, “Real-time high-resolution x-ray computed tomography,” inProceedings of the 38th ACM International Conference on Supercomputing, ser. ICS ’24. New York, NY , USA: Association for Computing Ma...

    2024

  35. [43]

    Leveraging gpu tensor cores for double precision euclidean distance calculations,

    B. Gallet and M. Gowanlock, “Leveraging gpu tensor cores for double precision euclidean distance calculations,” in2022 IEEE 29th International Conference on High Performance Computing, Data, and Analytics (HiPC), 2022, pp. 135–144. [Online]. Available: https://doi.org/10.1109/...

    2022

  36. [44]

    3.2 the a100 datacenter gpu and ampere architecture,

    J. Choquette, E. Lee, R. Krashinsky, V . Balan, and B. Khailany, “3.2 the a100 datacenter gpu and ampere architecture,” in2021 IEEE International Solid-State Circuits Conference (ISSCC), vol. 64, 2021, pp. 48–50. [Online]. Available: https://doi.org/10.1109/ISSCC42613. 2021.9365803

    2021

  37. [45]

    Nvidia hopper h100 gpu: Scaling performance,

    J. Choquette, “Nvidia hopper h100 gpu: Scaling performance,” IEEE Micro, vol. 43, no. 3, pp. 9–17, 2023. [Online]. Available: https://doi.org/10.1109/MM.2023.3256796

    2023

  38. [46]

    Dtc-spmm: Bridging the gap in accelerating general sparse matrix multiplication with tensor cores,

    R. Fan, W. Wang, and X. Chu, “Dtc-spmm: Bridging the gap in accelerating general sparse matrix multiplication with tensor cores,” inProceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3, 2024, pp...

    2024

  39. [47]

    CuTe Layout Representation and Algebra,

    C. Cecka, “CuTe Layout Representation and Algebra,”arXiv preprint arXiv:2603.02298, 2026. [Online]. Available: https://doi.org/10.48550/ arXiv.2603.02298

    2026 doi arXiv

  40. [48]

    Matrix is all you need: Rearchitecting quantum chemistry to scale on ai accelerators,

    H. Han, K. Li, F. Ju, Q. Li, H. An, Y . Chen, Y . Zhang, T. Cao, and M. Yang, “Matrix is all you need: Rearchitecting quantum chemistry to scale on ai accelerators,” inProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis,...

    2025

  41. [49]

    Performance and its limits in rigid body protein-protein docking,

    I. T. Desta, K. A. Porter, B. Xia, D. Kozakov, and S. Vajda, “Performance and its limits in rigid body protein-protein docking,” Structure, vol. 28, no. 9, pp. 1071–1081, 2020. [Online]. Available: https://doi.org/10.1016/j.str.2020.06.006

    2020 doi

  42. [50]

    Modelling protein docking using shape complementarity, electrostatics and biochemical information,

    H. A. Gabb, R. M. Jackson, and M. J. Sternberg, “Modelling protein docking using shape complementarity, electrostatics and biochemical information,”Journal of molecular biology, vol. 272, no. 1, pp. 106–120,

  43. [51]

    Haddock: a protein- protein docking approach based on biochemical or biophysical information,

    C. Dominguez, R. Boelens, and A. M. Bonvin, “Haddock: a protein- protein docking approach based on biochemical or biophysical information,”Journal of the American Chemical Society, vol. 125, no. 7, pp. 1731–1737, 2003. [Online]. Available: https: //doi.org/10.1021/ja026939x

    2003 doi

  44. [52]

    The light- dock server: Artificial intelligence-powered modeling of macromolecular interactions,

    B. Jim ´enez-Garc´ıa, J. Roel-Touris, and D. Barradas-Bautista, “The light- dock server: Artificial intelligence-powered modeling of macromolecular interactions,”Nucleic acids research, vol. 51, no. W1, pp. W298–W304,

  45. [53]

    An overview of the bluegene/l supercomputer,

    N. R. Adiga, G. Alm ´asi, G. S. Almasi, Y . Aridor, R. Barik, D. Beece, R. Bellofatto, G. Bhanot, R. Bickford, M. Blumrichet al., “An overview of the bluegene/l supercomputer,” inSC’02: Proceedings of the 2002 ACM/IEEE Conference on Supercomputing. IEEE, 2002, pp. 60–60. [Onli...

    2002 arXiv

  46. [54]

    Tsubame2.0: The first petascale supercomputer in japan and the greatest production in the world,

    S. Matsuoka, T. Aoki, T. Endo, H. Sato, S. Takizawa, A. Nomura, and K. Sato, “Tsubame2.0: The first petascale supercomputer in japan and the greatest production in the world,” inContemporary High Performance Computing. Chapman and Hall/CRC, 2017, pp. 525–555. [Online]. Availab...

    2017 doi

  47. [55]

    Juno: Optimizing high-dimensional approximate nearest neighbour search with sparsity-aware algorithm and ray-tracing core mapping,

    Z. Liu, W. Ni, J. Leng, Y . Feng, C. Guo, Q. Chen, C. Li, M. Guo, and Y . Zhu, “Juno: Optimizing high-dimensional approximate nearest neighbour search with sparsity-aware algorithm and ray-tracing core mapping,” inProceedings of the 29th ACM International Conference on Archite...

    2024

  48. [56]

    Demysti- fying the nvidia ampere architecture through microbenchmarking and instruction-level analysis,

    H. Abdelkhalik, Y . Arafa, N. Santhi, and A.-H. A. Badawy, “Demysti- fying the nvidia ampere architecture through microbenchmarking and instruction-level analysis,” in2022 IEEE High Performance Extreme Computing Conference (HPEC). Ieee, 2022, pp. 1–8. [Online]. Available: http...

    2022

  49. [57]

    A framework for performance modeling and prediction,

    A. Snavely, L. Carrington, N. Wolter, J. Labarta, R. Badia, and A. Purkayastha, “A framework for performance modeling and prediction,” inSC’02: Proceedings of the 2002 ACM/IEEE Conference on Supercomputing. IEEE, 2002, pp. 21–21. [Online]. Available: https://doi.org/10.1109/SC...

    2002 arXiv

  50. [58]

    Atr-hippo drives force signaling to nuclear f-actin and links mechanotransduction to neurological disorders,

    M. Chatzifrangkeskou, T. Stanly, D. Koennig, L. Campos-Soares, M. Eyres, A. Hasson, A. Perdiou, I. Vendrell, R. Fischer, S. Das et al., “Atr-hippo drives force signaling to nuclear f-actin and links mechanotransduction to neurological disorders,”Science Advances, vol. 11, no. ...

    2025 doi

  51. [59]

    Structural insights into ternary immunocomplex formation and cross-reactivity: binding of an anti- immunocomplex fabb12 to fab220-testosterone complex,

    V . Eronen, K. Takkinen, A. Torni, K. Peng, J. J ¨anis, T. Parkkinen, N. Hakulinen, and J. Rouvinen, “Structural insights into ternary immunocomplex formation and cross-reactivity: binding of an anti- immunocomplex fabb12 to fab220-testosterone complex,”The FEBS Journal, vol. ...

    2024 doi

  52. [60]

    Updates to the integrated protein–protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2,

    T. Vreven, I. H. Moal, A. Vangone, B. G. Pierce, P. L. Kastritis, M. Torchala, R. Chaleil, B. Jim ´enez-Garc´ıa, P. A. Bates, J. Fernandez-Recioet al., “Updates to the integrated protein–protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version...

    2015 doi

  53. [61]

    Dockq v2: Improved automatic quality measure for protein multimers, nucleic acids, and small molecules,

    C. Mirabello and B. Wallner, “Dockq v2: Improved automatic quality measure for protein multimers, nucleic acids, and small molecules,”Bioinformatics, pp. 2024–05, 2024. [Online]. Available: https://doi.org/10.1093/bioinformatics/btae586 13

    2024 doi

  54. [1997]

    Available: https://doi.org/10.1006/jmbi.1997.1203

    [Online]. Available: https://doi.org/10.1006/jmbi.1997.1203

    1997

  55. [2006]

    Available: https://doi.org/10.1002/prot.21117

    [Online]. Available: https://doi.org/10.1002/prot.21117

    doi

  56. [2017]

    Available: https://doi.org/10.1038/s41598-017-08414-y

    [Online]. Available: https://doi.org/10.1038/s41598-017-08414-y

    doi

  57. [2023]

    Available: https://doi.org/10.1093/nar/gkad327

    [Online]. Available: https://doi.org/10.1093/nar/gkad327

    doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.