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Leonardo Solis-Vasquez
Person information
- affiliation: Technical University of Darmstadt, Germany
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2020 – today
- 2024
- [c13]Jonathan Strobl
, Leonardo Solis-Vasquez
Graphtoy: Fast Software Simulation of Applications for AMD's AI Engines. ARC 2024: 166-180 - 2023
- [c12]Leonardo Solis-Vasquez
Experiences Migrating CUDA to SYCL: A Molecular Docking Case Study. IWOCL 2023: 15:1-15:11 - [c11]Christoph Weckert
Altis-SYCL: Migrating Altis Benchmarking Suite from CUDA to SYCL for GPUs and FPGAs. SC Workshops 2023: 547-555 - 2022
- [j6]Lukas Weber
On the necessity of explicit cross-layer data formats in near-data processing systems. Distributed Parallel Databases 40(1): 27-45 (2022) - [j5]Leonardo Solis-Vasquez
Benchmarking the performance of irregular computations in AutoDock-GPU molecular docking. Parallel Comput. 109: 102861 (2022) - [j4]Tobias Vinçon, Christian Knödler, Leonardo Solis-Vasquez
Near-Data Processing in Database Systems on Native Computational Storage under HTAP Workloads. Proc. VLDB Endow. 15(10): 1991-2004 (2022) - [c10]Tobias Vinçon, Christian Knödler, Arthur Bernhardt, Leonardo Solis-Vasquez
Result-Set Management for NDP Operations on Smart Storage. DaMoN 2022: 12:1-12:5 - 2021
- [j3]Leonardo Solis-Vasquez
Porting and Optimizing Molecular Docking onto the SX-Aurora TSUBASA Vector Computer. Supercomput. Front. Innov. 8(2): 27-42 (2021) - [c9]Christian Knödler, Tobias Vinçon, Arthur Bernhardt, Ilia Petrov, Leonardo Solis-Vasquez
A cost model for NDP-aware query optimization for KV-stores. DaMoN 2021: 12:1-12:5 - [c8]Lukas Weber, Lukas Sommer, Leonardo Solis-Vasquez
A Framework for the Automatic Generation of FPGA-based Near-Data Processing Accelerators in Smart Storage Systems. IPDPS Workshops 2021: 136-143 - [c7]Leonardo Solis-Vasquez
Mapping Irregular Computations for Molecular Docking to the SX-Aurora TSUBASA Vector Engine. IA3@SC 2021: 1-10 - 2020
- [c6]Scott LeGrand, Aaron Scheinberg, Andreas F. Tillack, Mathialakan Thavappiragasam, Josh Vincent Vermaas
GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research. BCB 2020: 43:1-43:10 - [c5]Lukas Sommer
Using Parallel Programming Models for Automotive Workloads on Heterogeneous Systems - a Case Study. PDP 2020: 17-21 - [c4]Leonardo Solis-Vasquez
Evaluating the Energy Efficiency of OpenCL-accelerated AutoDock Molecular Docking. PDP 2020: 162-166 - [c3]Leonardo Solis-Vasquez
Parallelizing Irregular Computations for Molecular Docking. IA3@SC 2020: 12-21
2010 – 2019
- 2019
- [b1]Leonardo Solis-Vasquez:
Accelerating Molecular Docking by Parallelized Heterogeneous Computing - A Case Study of Performance, Quality of Results, and Energy-Efficiency using CPUs, GPUs, and FPGAs. Darmstadt University of Technology, Germany, 2019 - [j2]Lea El-Khoury, Diogo Santos-Martins
Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4. J. Comput. Aided Mol. Des. 33(12): 1011-1020 (2019) - [j1]Diogo Santos-Martins
D3R Grand Challenge 4: prospective pose prediction of BACE1 ligands with AutoDock-GPU. J. Comput. Aided Mol. Des. 33(12): 1071-1081 (2019) - [c2]Lukas Sommer
DAPHNE - An automotive benchmark suite for parallel programming models on embedded heterogeneous platforms: work-in-progress. EMSOFT Companion 2019: 4 - 2017
- [c1]Leonardo Solis-Vasquez
A Performance and Energy Evaluation of OpenCL-accelerated Molecular Docking. IWOCL 2017: 3:1-3:11
Coauthor Index
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