Leonardo Solis-Vasquez

Orcid: 0000-0001-6896-9879

Affiliations:
  • Technical University of Darmstadt, Germany


According to our database1, Leonardo Solis-Vasquez authored at least 20 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Graphtoy: Fast Software Simulation of Applications for AMD's AI Engines.
Proceedings of the Applied Reconfigurable Computing. Architectures, Tools, and Applications, 2024

2023
Altis-SYCL: Migrating Altis Benchmarking Suite from CUDA to SYCL for GPUs and FPGAs.
Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, 2023

Experiences Migrating CUDA to SYCL: A Molecular Docking Case Study.
Proceedings of the 2023 International Workshop on OpenCL, 2023

2022
Near-Data Processing in Database Systems on Native Computational Storage under HTAP Workloads.
Proc. VLDB Endow., 2022

Benchmarking the performance of irregular computations in AutoDock-GPU molecular docking.
Parallel Comput., 2022

On the necessity of explicit cross-layer data formats in near-data processing systems.
Distributed Parallel Databases, 2022

Result-Set Management for NDP Operations on Smart Storage.
Proceedings of the International Conference on Management of Data, 2022

2021
Porting and Optimizing Molecular Docking onto the SX-Aurora TSUBASA Vector Computer.
Supercomput. Front. Innov., 2021

Mapping Irregular Computations for Molecular Docking to the SX-Aurora TSUBASA Vector Engine.
Proceedings of the 11th IEEE/ACM Workshop on Irregular Applications: Architectures and Algorithms, 2021

A Framework for the Automatic Generation of FPGA-based Near-Data Processing Accelerators in Smart Storage Systems.
Proceedings of the IEEE International Parallel and Distributed Processing Symposium Workshops, 2021

A cost model for NDP-aware query optimization for KV-stores.
Proceedings of the 17th International Workshop on Data Management on New Hardware, 2021

2020
Parallelizing Irregular Computations for Molecular Docking.
Proceedings of the 10th IEEE/ACM Workshop on Irregular Applications: Architectures and Algorithms, 2020

Using Parallel Programming Models for Automotive Workloads on Heterogeneous Systems - a Case Study.
Proceedings of the 28th Euromicro International Conference on Parallel, 2020

Evaluating the Energy Efficiency of OpenCL-accelerated AutoDock Molecular Docking.
Proceedings of the 28th Euromicro International Conference on Parallel, 2020

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research.
Proceedings of the BCB '20: 11th ACM International Conference on Bioinformatics, 2020

2019
Accelerating Molecular Docking by Parallelized Heterogeneous Computing - A Case Study of Performance, Quality of Results, and Energy-Efficiency using CPUs, GPUs, and FPGAs.
PhD thesis, 2019

D3R Grand Challenge 4: prospective pose prediction of BACE1 ligands with AutoDock-GPU.
J. Comput. Aided Mol. Des., 2019

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., 2019

DAPHNE - An automotive benchmark suite for parallel programming models on embedded heterogeneous platforms: work-in-progress.
Proceedings of the International Conference on Embedded Software Companion, 2019

2017
A Performance and Energy Evaluation of OpenCL-accelerated Molecular Docking.
Proceedings of the 5th International Workshop on OpenCL, 2017


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