# Scalable High-Fidelity Macromolecular Docking for GPU-Accelerated Supercomputers

arXiv: https://arxiv.org/abs/2608.07078 · HTML: https://mlsanity.com/paper/2608.07078/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 61 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [NVIDIA Tensor Core Programmability, Performance & Precision](https://mlsanity.com/paper/1803.04014/) (2018)
- [Demystifying the Nvidia Ampere Architecture through Microbenchmarking and Instruction-level Analysis](https://mlsanity.com/paper/2208.11174/) (2022)
- [CuTe Layout Representation and Algebra](https://mlsanity.com/paper/2603.02298/) (2026)
- [JUNO: Optimizing High-Dimensional Approximate Nearest Neighbour Search with Sparsity-Aware Algorithm and Ray-Tracing Core Mapping](https://mlsanity.com/paper/2312.01712/) (2023)
- [Leveraging GPU Tensor Cores for Double Precision Euclidean Distance Calculations](https://mlsanity.com/paper/2209.11287/) (2022)

## Abstract (arXiv)

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 × and 18.9 × 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.