Events:
[ MLSys'20 reproducibility initiative ]
[ MLSys'19 reproducibility initiative ] [ Index ]
Paper | Artifact available | Artifact functional and reusable | Some results replicated | Automation |
1) AggregaThor: Byzantine Machine Learning Georgios Damaskinos, El Mahdi El Mhamdi, Rachid Guerraoui, Arsany Guirguis, Sebastien Rouault
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10.5281/zenodo.2548779 |
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✔GitHub |
GitHub |
2) Optimizing DNN Computation with Relaxed Graph Substitutions Zhihao Jia, James Thomas, Todd Warszawski, Mingyu Gao, Matei Zaharia, Alex Aiken
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10.5281/zenodo.2549853 |
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Script |
3) Learning kernels that adapt to GPUs Siyuan Ma, Mikhail Belkin
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10.5281/zenodo.2574996 |
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Script |
4) Priority-based Parameter Propagation for Distributed DNN Training Anand Jayarajan, Jinliang Wei, Garth Gibson, Alexandra Fedorova, Gennady Pekhimenko
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10.5281/zenodo.2549852 |
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✔GitHub(open evaluation example) |
GitHub |
5) Beyond Data and Model Parallelism for Deep Neural Networks Zhihao Jia, Matei Zaharia, Alex Aiken
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10.5281/zenodo.2549847 |
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Script |
[ PPoPP'19 reproducibility initiative ] [ Index ]
Paper | Artifact available | Artifact functional | Artifact reusable | Results reproduced | Results replicated |
1) A Round-Efficient Distributed Betweenness Centrality Algorithm Loc Hoang, Matteo Pontecorvi, Roshan Dathathri, Gurbinder Gill, Bozhi You, Keshav Pingali, Vijaya Ramachandran
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10.5281/zenodo.2399798 |
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2) A Pattern Based Algorithmic Autotuner for Graph Processing on GPUs Ke meng, Jiajia Li, Guangming Tan, Ninghui Sun
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10.5281/zenodo.149090 |
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3) Transitive Joins: A Sound and Efficient Online Deadlock-Avoidance Policy Caleb Voss, Tiago Cogumbreiro, Vivek Sarkar
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4) Encapsulated Open Nesting for STM: Fine-Grained Higher-Level Conflict Detection Martin Bättig, Thomas Gross
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5) Reliable Group Communication using Corrected Trees Martin Küttler, Maksym Planeta, Jan Bierbaum, Carsten Weinhold, Hermann Härtig, Amnon Barak, Torsten Hoefler
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10.5281/zenodo.1493446 |
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6) Proactive Work Stealing for Futures Kyle Singer, Yifan Xu, I-Ting Angelina Lee
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10.5281/zenodo.2227457 |
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7) Semantics-Aware Scheduling Policies for Synchronization Determinism Qi Zhao, Zhengyi Qiu, Guoliang Jin
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10.1145/3300171 |
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8) Adaptive Sparse Matrix-Matrix Multiplication on the GPU Martin Winter, Daniel Mlakar, Rhaleb Zayer, Hans-Peter Seidel, Markus Steinberger
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10.1145/3300172 |
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9) Incremental Flattening for Nested Data Parallelism Troels Henriksen, Frederik Thorøe, Martin Elsman, Cosmin E. Oancea
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10.1145/3300173 |
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10) Beyond Human-Level Accuracy: Computational Challenges in Deep Learning Joel Hestness, Newsha Ardalani, Gregory Diamos
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10.5281/zenodo.2259280 |
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11) QTLS: High-Performance TLS Asynchronous Offload Framework with Intel QuickAssist Technology Xiaokang Hu, Changzheng Wei, Jian Li, Brian Will, Ping Yu, Lu Gong, Haibing Guan
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10.5281/zenodo.2008661 |
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12) Checking Linearizability Using Hitting Families Burcu Kulahcioglu Ozkan, Rupak Majumdar, Filip Niksic
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10.5281/zenodo.1890165 |
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13) Efficient Race Detection with Futures Robert Utterback, Kunal Agrawal, Jeremy Fineman, I-Ting Angelina Lee
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10.5281/zenodo.2510564 |
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14) Provably and Practically Efficient Granularity Control Umut A. Acar, Vitaly Aksenov, Arthur Chargueraud, Mike Rainey
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15) Leveraging Hardware TM in Haskell Ryan Yates, Michael Scott
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10.5281/zenodo.1998472 |
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16) Stretching the capacity of Hardware Transactional Memory in IBM POWER architectures Ricardo Filipe, Shady Issa, Paolo Romano, João Barreto
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10.6084/m9.figshare.7378496.v2 |
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17) SEP-Graph: Finding Shortest Execution Paths for Graph Processing under a Hybrid Framework on GPU Hao Wang, Liang Geng, Rubao Lee, Kaixi Hou, Yanfeng Zhang, Xiaodong Zhang
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10.5281/zenodo.2008653 10.5281/zenodo.2008655 |
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18) A Coordinated Tiling and Batching Framework for Efficient GEMM on GPUs Xiuhong Li, Yun Liang, Shengen Yan, Liancheng Jia, Yinghan Li
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10.1145/3300174 |
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19) Lightweight Hardware Transactional Memory Profiling Qingsen Wang, Pengfei Su, Milind Chabbi, Xu Liu
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10.1145/3300175 |
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20) Adaptive Sparse Tiling for Sparse Matrix Multiplication Changwan Hong, Aravind Sukumaran-Rajam, Israt Nisa, Kunal Singh, P. Sadayappan
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Paper | Artifact available | Artifact functional | Artifact reusable | Results reproduced | Results replicated |
Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe Jiong Gong, Haihao Shen, Guoming Zhang, Xiaoli Liu, Shane Li, Ge Jin, Niharika Maheshwari, Evarist Fomenko, Eden Segal [ Paper DOI ] [ Artifact DOI ] [ Original artifact ] [ CK workflow ] [ CK results ] |
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Optimizing Deep Learning Workloads on ARM GPU with TVM Lianmin Zheng, Tianqi Chen [ Paper DOI ] [ Artifact DOI ] [ Original artifact ] [ CK workflow ] [ CK results ] |
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Real-Time Image Recognition Using Collaborative IoT Devices Ramyad Hadidi, Jiashen Cao, Matthew Woodward, Michael S. Ryoo, Hyesoon Kim [ Paper DOI ] [ Artifact DOI ] [ Original artifact ] [ CK workflow ] [ CK results ] |
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Leveraging the VTA-TVM Hardware-Software Stack for FPGA Acceleration of 8-bit ResNet-18 Inference Thierry Moreau [ Paper DOI ] [ Artifact DOI ] [ Original artifact ] [ CK workflow ] [ CK results ] |
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Multi-objective autotuning of MobileNets across the full software/hardware stack Anton Lokhmotov, Nikolay Chunosov, Flavio Vella, Grigori Fursin [ Paper DOI ] [ Artifact DOI ] [ CK workflow ] [ CK results ] |
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This year we successfully validated the new ACM Artifact Review and Badging policy which we co-authored last year. However, we also noticed that "reusability/customization" criteria in the new guidelines are quite vague and caused ambiguity in evaluation of several complex artifacts. We would like to discuss this issue further with the community and develop more precise guidelines for next artifact evaluation. In the mean time, since our philosophy of artifact evaluation is that it is a cooperative process between authors and reviewers to overcome technical issues together, we helped all authors improve artifacts and pass evaluation.
Paper | Artifact available | Artifact functional | Artifact reusable | Results replicated |
Optimizing N-Dimensional, Winograd-Based Convolution for Manycore CPUs Zhen Jia, Aleksandar Zlateski, Frédo Durand and Kai Li |
Artifact (10.6084/m9.figshare.5873868) |
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PAM: Parallel Augmented Maps Yihan Sun, Daniel Ferizovic and Guy Blelloch |
Artifact (10.5281/zenodo.1168703) |
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An Effective Fusion and Tile Size Model for Optimizing Image Processing Pipelines Abhinav Jangda and Uday Bondhugula |
Artifact (10.5281/zenodo.1168539) |
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Cache-Tries: Concurrent Lock-Free Hash Tries with Constant-Time Operations Aleksandar Prokopec |
Artifact (10.5281/zenodo.1168402) |
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swSpTRSV: a Fast Sparse Triangular Solve with Sparse Level Tile Layout on Sunway Architectures Xinliang Wang, Weifeng Liu, Wei Xue and Li Wu |
Artifact (10.5281/zenodo.1168762) |
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VerifiedFT: A Verified, High-Performance Dynamic Race Detector James Wilcox, Cormac Flanagan and Stephen Freund |
Artifact (10.5281/zenodo.1171046) |
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Juggler: A Dependency-Aware Task Based Execution Framework for GPUs Mehmet Belviranli, Seyong Lee, Jeff Vetter and Laxmi Bhuyan |
Artifact (10.5281/zenodo.1168558) |
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Bridging the Gap between Deep Learning and Sparse Matrix Format Selection Yue Zhao, Jiajia Li, Chunhua Liao and Xipeng Shen |
Artifact (10.6084/m9.figshare.5873868) |
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Harnessing Epoch-based Reclamation for Efficient Range Queries Maya Arbel and Trevor Brown |
Artifact (10.5281/zenodo.1168726) |
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Register Optimizations for Stencils on GPUs Prashant Rawat, Aravind Sukumaran-Rajam, Atanas Rountev, Fabrice Rastello, Louis-Noel Pouchet and P. Sadayappan |
Artifact (10.5281/zenodo.1168498) |
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Making Pull-Based Graph Processing Performant Samuel Grossman, Heiner Litz and Christos Kozyrakis |
Artifact (10.5281/zenodo.1169388) |
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Interval-Based Memory Reclamation Haosen Wen, Joseph Izraelevitz, Wentao Cai, H. Alan Beadle and Michael L. Scott |
Artifact (10.5281/zenodo.1168572) |
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Efficient Parallel Race Detection for Two-Dimensional Dags Yifan Xu, Kunal Agrawal and I-Ting Angelina Lee |
Artifact (10.5281/zenodo.1169390) |
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Featherlight On-the-fly False-sharing Detection Milind Chabbi, Shasha Wen and Xu Liu |
Artifacts: 1, 2, 3, 4, 5 (10.5281/zenodo.1168535) (10.5281/zenodo.1168520) (10.5281/zenodo.1168529) (10.5281/zenodo.1168533) (10.5281/zenodo.1168526) |
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Communication-Avoiding Minimum Cuts and Connected Components Pavel Kalvoda, Lukas Gianinazzi, Alessandro De Palma, Maciej Besta and Torsten Hoefler |
Artifact (10.5281/zenodo.1169439) |
This year we successfully validated the new ACM Artifact Review and Badging policy which we co-authored last year.
Paper | Artifact available | Artifact functional | Artifact reusable | Results replicated |
Poker: Permutation-based SIMD Execution of Intensive Tree Search by Path Encoding F. Zhang, J. Xue |
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Optimal DNN Primitive Selection with Partitioned Boolean Quadratic Programming A. Anderson, D. Gregg |
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May-Happen-in-Parallel Analysis with Static Vector Clocks Q. Zhou, L. Li, L. Wang, J. Xue, X. Feng |
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SIMD Intrinsics on Managed Language Runtimes Alen Stojanov, Ivaylo Toskov, Tiark Rompf, and Markus Püschel |
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DeLICM: Scalar Memory Dependence Removal at Zero Memory Cost M. Kruse, T. Grosser |
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CVR: Efficient SpMV Vectorization on X86 Processors B. Xie, J. Zhan, X. Liu, Z. Jia, W. Gao, L. Zhang |
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Lightweight Detection of Cache Conflicts P. Roy, S. Song, S. Krishnamoorthy, X. Liu |
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Synthesizing Instruction Selection S. Buchwald, A. Fried, S. Hack |
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Qubit Allocation M. Siraichi, V. Santos, S. Collange, F. Pereira |
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High Performance Stencil Code Generation with LIFT B. Hagedorn, L. Stoltzfus, M. Steuwer, S. Gorlatch, C. Dubach |
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Conflict-free Vectorization of Associative Irregular Applications with Recent SIMD Architectural Advances P. Jiang, G. Agrawal |
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nAdroid: Statically Detecting Ordering Violations in Android Applications X. Fu, D. Lee, C. Jung |
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CUDAAdvisor: LLVM-based Runtime Profiling for Modern GPUs D. Shen, S. Song, A. Li, X. Liu |
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Enabling Enclave Code Secrecy via Self-Modification E. Bauman, H. Wang, M. Zhang, Z. Lin |
A Compiler for Cyber-Physical Digital Microfluidic Biochips Christopher Curtis, Daniel Grissom, Philip Brisk We also encourage sharing of artifacts via ACM DL even if they were not submitted for evaluation due to lack of time, etc. However, we now collaborate with ACM to develop common formats and API for such artifacts. |
This year we successfully tried an "open reviewing model" with a few artifacts, when we asked the community to publicly evaluate several artifacts already available at GitHub, GitLab and other project hosting services. This allowed us to find external reviewers who had access to very rare HPC servers or proprietary benchmarks and tools. We also allowed authors of the accepted artifacts to add up to 2 pages of Artifact Appendix to their camera-ready paper and let readers better understand what was evaluated and how.
This year we successfully tried an "open reviewing model" with a few artifacts, when we asked the community to publicly evaluate several artifacts already available at GitHub, GitLab and other project hosting services. This allowed us to find external reviewers who had access to very rare HPC servers or proprietary benchmarks and tools. We also allowed authors of the accepted artifacts to add up to 2 pages of Artifact Appendix to their camera-ready paper and let readers better understand what was evaluated and how.
[ Event website ]
[ Index ]
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