| 59 |
Efficiently Compiling Efficient Query Plans for Modern Hardware |
2011 |
VLDB |
0.0006445664 |
| 116 |
Eddies: Continuously Adaptive Query Processing |
2000 |
SIGMOD |
0.00046191288 |
| 411 |
PyTorch Distributed: Experiences on Accelerating Data Parallel Training |
2020 |
VLDB |
0.00023881138 |
| 650 |
Robust Query Processing through Progressive Optimization |
2004 |
SIGMOD |
0.0001865144 |
| 1,407 |
Hybrid Parallelization Strategies for Large-Scale Machine Learning in SystemML |
2014 |
VLDB |
0.00012163413 |
| 1,875 |
An Architecture for Compiling UDF-centric Workflows |
2015 |
VLDB |
0.00010243959 |
| 1,884 |
Tuplex: Data Science in Python at Native Code Speed |
2021 |
SIGMOD |
0.00010206514 |
| 2,072 |
HippogriffDB: Balancing I/O and GPU Bandwidth in Big Data Analytics |
2016 |
VLDB |
9.6300019e-05 |
| 2,090 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
9.5668285e-05 |
| 2,292 |
Pipelined Query Processing in Coprocessor Environments |
2018 |
SIGMOD |
9.0884645e-05 |
| 2,690 |
Accelerating Recommendation System Training by Leveraging Popular Choices |
2022 |
VLDB |
8.2911466e-05 |
| 2,904 |
Evaluating End-to-End Optimization for Data Analytics Applications in Weld |
2018 |
VLDB |
7.9403097e-05 |
| 3,028 |
NeutronStar: Distributed GNN Training with Hybrid Dependency Management |
2022 |
SIGMOD |
7.6833093e-05 |
| 3,920 |
On Optimizing Operator Fusion Plans for Large-Scale Machine Learning in SystemML |
2018 |
VLDB |
6.6246708e-05 |
| 4,807 |
Resource Elasticity for Large-Scale Machine Learning |
2015 |
SIGMOD |
5.9045148e-05 |
| 4,952 |
Designing an Open Framework for Query Optimization and Compilation |
2022 |
VLDB |
5.806142e-05 |
| 5,089 |
TCUDB: Accelerating Database with Tensor Processors |
2022 |
SIGMOD |
5.7017353e-05 |
| 5,169 |
HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training |
2022 |
SIGMOD |
5.642415e-05 |
| 5,332 |
Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce |
2021 |
SIGMOD |
5.5640779e-05 |
| 5,992 |
NuPS: A Parameter Server for Machine Learning with Non-Uniform Parameter Access |
2022 |
SIGMOD |
5.2380905e-05 |
| 6,361 |
Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism |
2023 |
VLDB |
5.0903244e-05 |
| 6,367 |
Improving Execution Efficiency of Just-in-time Compilation based Query Processing on GPUs |
2021 |
VLDB |
5.0887599e-05 |
| 6,649 |
Grizzly: Efficient Stream Processing Through Adaptive Query Compilation |
2020 |
SIGMOD |
4.9724735e-05 |
| 7,828 |
Measuring and Optimizing Distributed Array Programs |
2016 |
VLDB |
4.6374634e-05 |
| 8,258 |
FuseME: Distributed Matrix Computation Engine based on Cuboid-based Fused Operator and Plan Generation |
2022 |
SIGMOD |
4.5424271e-05 |
| 8,477 |
Excalibur: A Virtual Machine for Adaptive Fine-grained JIT-Compiled Query Execution based on VOILA |
2023 |
VLDB |
4.4971772e-05 |
| 8,605 |
Harmony: Overcoming the Hurdles of GPU Memory Capacity to Train Massive DNN Models on Commodity Servers |
2022 |
VLDB |
4.4813623e-05 |
| 8,988 |
Optimizing Inference Serving on Serverless Platforms |
2022 |
VLDB |
4.4123773e-05 |
| 9,271 |
COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression |
2022 |
VLDB |
4.3625977e-05 |
| 9,704 |
ETO: Accelerating Optimization of DNN Operators by High-Performance Tensor Program Reuse |
2022 |
VLDB |
4.2953234e-05 |