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 |
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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. |
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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 ]