| Nome |
# |
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A survey on compiler autotuning using machine learning, file e0c31c0c-fe4e-4599-e053-1705fe0aef77
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1.062
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Automatic Tuning of Compilers Using Machine Learning, file e0c31c0d-07a1-4599-e053-1705fe0aef77
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639
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SOCRATES - A seamless online compiler and system runtime autotuning framework for energy-aware applications, file e0c31c0c-bbd8-4599-e053-1705fe0aef77
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326
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Accelerating a Geometric Approach to Molecular Docking with OpenACC, file e0c31c0d-19f3-4599-e053-1705fe0aef77
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322
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Autotuning and adaptivity approach for energy efficient Exascale HPC systems: The ANTAREX approach, file e0c31c0c-f56f-4599-e053-1705fe0aef77
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277
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MiCOMP: Mitigating the Compiler Phase-Ordering Problem Using Optimization Sub-Sequences and Machine Learning, file e0c31c0d-15a5-4599-e053-1705fe0aef77
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269
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ANTAREX - AutoTuning and adaptivity approach for energy efficient eXascale HPC systems, file e0c31c0c-c110-4599-e053-1705fe0aef77
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252
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The ANTAREX tool flow for monitoring and autotuning energy efficient HPC systems, file e0c31c0c-cc98-4599-e053-1705fe0aef77
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248
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Exploiting OpenMP and OpenACC to accelerate a geometric approach to molecular docking in heterogeneous HPC nodes, file e0c31c0e-71ef-4599-e053-1705fe0aef77
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206
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The ANTAREX approach to autotuning and adaptivity for energy efficient HPC Systems, file e0c31c0c-f569-4599-e053-1705fe0aef77
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188
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Tunable approximations to control time-to-solution in an HPC molecular docking Mini-App, file e0c31c10-b0f2-4599-e053-1705fe0aef77
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167
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The ANTAREX domain specific language for high performance computing, file e0c31c0f-0165-4599-e053-1705fe0aef77
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155
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An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System, file e0c31c0e-ea2a-4599-e053-1705fe0aef77
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153
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Autotuning and adaptivity in energy efficient HPC systems: The ANTAREX toolbox, file e0c31c0c-f25d-4599-e053-1705fe0aef77
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108
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SPIRIT: Spectral-Aware Pareto Iterative Refinement Optimization for Supervised High-Level Synthesis, file e0c31c0e-ba41-4599-e053-1705fe0aef77
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102
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An extreme-scale virtual screening platform for drug discovery, file b0df9fcb-95c2-42ee-9582-3ac6e4ff24ec
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90
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EXSCALATE: An Extreme-Scale Virtual Screening Platform for Drug Discovery Targeting Polypharmacology to Fight SARS-CoV-2, file 1ee17f6c-9609-421a-9956-cbef89a1388f
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88
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DeSpErate++: An enhanced design space exploration framework using predictive simulation scheduling, file e0c31c0e-c04a-4599-e053-1705fe0aef77
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73
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mARGOt: a Dynamic Autotuning Framework for Self-aware Approximate Computing, file e0c31c0f-278d-4599-e053-1705fe0aef77
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71
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An Hybrid Approach to accelerate a Molecular Docking Application for Virtual Screening in Heterogeneous Nodes, file e0c31c10-9842-4599-e053-1705fe0aef77
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66
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Pegasus: Performance Engineering for Software Applications Targeting HPC Systems, file e0c31c10-e22e-4599-e053-1705fe0aef77
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55
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Workload- and process-variation aware voltage/frequency tuning for energy efficient performance sustainability of NTC manycores, file e0c31c0f-27fd-4599-e053-1705fe0aef77
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54
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Design space pruning and computational workload splitting for autotuning OpenCL applications, file e0c31c0c-bbda-4599-e053-1705fe0aef77
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46
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Tunable and Portable Extreme-Scale Drug Discovery Platform at Exascale: the LIGATE Approach, file f387b78b-0b0b-4e52-93d9-b9543d14997f
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29
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COBAYN: Compiler autotuning framework using Bayesian networks, file e0c31c12-6981-4599-e053-1705fe0aef77
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17
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Supporting the Scale-up of High Performance Application to Pre-Exascale Systems: The ANTAREX Approach, file e6f20c1e-7342-4f91-80c0-3effd0ed2e9e
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6
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SPIRIT: Spectral-Aware Pareto Iterative Refinement Optimization for Supervised High-Level Synthesis, file e0c31c07-be33-4599-e053-1705fe0aef77
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5
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ANTAREX: A DSL-based Approach to Adaptively Optimizing and Enforcing Extra-Functional Properties in High Performance Computing, file d64116db-5e42-4e0d-8cc8-c4f0c0eb9812
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4
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The first 25 years of the FPL conference: Significant papers, file e0c31c0c-bf2d-4599-e053-1705fe0aef77
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3
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A Tile-based Fused-layer CNN Accelerator for FPGAs, file e0c31c10-b6e1-4599-e053-1705fe0aef77
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3
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Runtime Design Space Exploration and Mapping of DCNNs for the Ultra-Low-Power Orlando SoC, file e0c31c10-d690-4599-e053-1705fe0aef77
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3
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Accelerating Binary and Mixed-Precision NNs Inference on STMicroelectronics Embedded NPU with Digital In-Memory-Computing, file d258da15-7a30-4c1a-9457-eab7fe83b0a2
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2
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Floorplan-aware hierarchical NoC topology with GALS interfaces, file e0c31c07-fdf4-4599-e053-1705fe0aef77
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2
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Tunable approximations to control time-to-solution in an HPC molecular docking Mini-App, file e0c31c0f-d2c1-4599-e053-1705fe0aef77
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2
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Proceedings of the Platform for Advanced Scientific Computing Conference, file 27713098-8e4d-49dc-aa24-ccd352b591e0
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1
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Floorplanning-aware design space exploration for application-specific hierarchical networks on-chip, file e0c31c07-f4a3-4599-e053-1705fe0aef77
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1
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MULTICUBE: Multi-objective Design Space Exploration of Multi-core Architectures, file e0c31c07-f4b6-4599-e053-1705fe0aef77
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1
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OSCAR: an Optimization Methodology Exploiting Spatial Correlation in Multi-core Design Spaces, file e0c31c07-fa4d-4599-e053-1705fe0aef77
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1
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Variation-aware voltage island formation for power efficient near-threshold manycore architectures, file e0c31c08-486b-4599-e053-1705fe0aef77
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1
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DeSpErate++: An enhanced design space exploration framework using predictive simulation scheduling, file e0c31c09-72c8-4599-e053-1705fe0aef77
|
1
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A system-level exploration of power delivery architectures for near-threshold manycores considering performance constraints, file e0c31c0a-eac4-4599-e053-1705fe0aef77
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1
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Predictive modeling methodology for compiler phase-ordering, file e0c31c0a-eac5-4599-e053-1705fe0aef77
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1
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Throughput balancing for energy efficient near-threshold manycores, file e0c31c0a-f509-4599-e053-1705fe0aef77
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1
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Autotuning and adaptivity in energy efficient HPC systems: The ANTAREX toolbox, file e0c31c0c-75a9-4599-e053-1705fe0aef77
|
1
|
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SOCRATES - A seamless online compiler and system runtime autotuning framework for energy-aware applications, file e0c31c0c-cfc6-4599-e053-1705fe0aef77
|
1
|
|
A survey on compiler autotuning using machine learning, file e0c31c0c-fe4f-4599-e053-1705fe0aef77
|
1
|
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Accelerating a Geometric Approach to Molecular Docking with OpenACC, file e0c31c0d-0057-4599-e053-1705fe0aef77
|
1
|
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Parallelized Convolutions for Embedded Ultra Low Power Deep Learning SoC, file e0c31c10-981e-4599-e053-1705fe0aef77
|
1
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Design Space Exploration for Orlando Ultra Low-Power Convolutional Neural Network SoC, file e0c31c10-981f-4599-e053-1705fe0aef77
|
1
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A tile-based fused-layer approach to accelerate DCNNs on low-density FPGAs, file e0c31c10-a979-4599-e053-1705fe0aef77
|
1
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A Bayesian network approach for compiler auto-tuning for embedded processors, file e0c31c10-c898-4599-e053-1705fe0aef77
|
1
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EXSCALATE: An Extreme-Scale Virtual Screening Platform for Drug Discovery Targeting Polypharmacology to Fight SARS-CoV-2, file f93ae434-ca91-4751-95fa-ba2cde117a3a
|
1
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| Totale |
5.111 |