Rammer Enabling Holistic Deep Learning Compiler Optimizations with rTasks. In OSDI 2020. USENIX Association 881–897. isbn 978-1-939133-19-9 Google Scholar Nimrod Megiddo and Vivek Sarkar. 1997. Optimal weighted loop fusion for parallel programs. In Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures. 282
2021-7-21 · Abstract High-performance tensor programs are crucial to guarantee efficient execution of deep neural networks. However obtaining performant tensor programs for different operators on various hardware platforms is notoriously challenging. Currently deep learning systems rely on vendor-provided kernel libraries or various search strategies to
2020-4-11 · Rammer achieves this by proposing several novel hardware neutral and clean abstractions for the computation tasks and the hardware accelerators. These abstractions expose a much richer scheduling space to Rammer which employs several heuristics to explore this space and finds efficient schedules. OSDI. This is an embedded video. Talk and
2021-7-19 · Rammer Enabling Holistic Deep Learning Compiler Optimizations with rTasks Lingxiao Ma Zhiqiang Xie Zhi Yang Jilong Xue Youshan Miao Wei Cui Wenxiang Hu Fan Yang Lintao Zhang Lidong Zhou the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20) 2020 HiveD Sharing a GPU Cluster for Deep Learning with Guarantees
2021-6-3 · OSDI 2020 RAMMER "" MSRA · 2020-11-12 17 43 50 T-ULRv2XTREME MSRA · 2020-11-04 18 54 31 OSDI 2020
2021-7-19 · Rammer Enabling Holistic Deep Learning Compiler Optimizations with rTasks Lingxiao Ma Zhiqiang Xie Zhi Yang Jilong Xue Youshan Miao Wei Cui Wenxiang Hu Fan Yang Lintao Zhang Lidong Zhou the 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20) 2020 HiveD Sharing a GPU Cluster for Deep Learning with Guarantees
AIIR zhuanlan.zhihu OSDI 20 RAMMER (NNFusion) zhuanlan.zhihu AI--Dynamic Shape Compiler zhuanlan.zhihu AI-- zhuanlan.zhihu auto vectorizationpolyhedral
To accelerate CNN inference existing deep learning frameworks focus on optimizing intra-operator parallelization. However a single operator can no longer fully utilize the available parallelism given the rapid advances in high-performance hardware resulting in a large gap between the peak performance and the real performance. This performance gap is more severe under smaller batch sizes.
Rammer Enabling Holistic Deep Learning Compiler Optimizations with rTasks. In OSDI 2020. USENIX Association 881–897. isbn 978-1-939133-19-9 Google Scholar Nimrod Megiddo and Vivek Sarkar. 1997. Optimal weighted loop fusion for parallel programs. In Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures. 282
2020-8-1 · " RAMMER Enabling Holistic Deep Learning Compiler Optimizations with rTasks " OSDI-2020 (a) (b) RAMMER
2020-8-20 · Rammer Enabling Holistic Deep Learning Compiler Optimizations with rTasks Publication Type Conference Paper Year of Publication 2020 Authors Ma L Xie Z Yang Z Xue J Miao Y Cui W Hu W Yang F Zhang L Zhou L Conference Name 14th USENIX Symposium on Operating Systems Design and Implementation ( OSDI 20) Date Published 11/2020
2020-8-1 · " RAMMER Enabling Holistic Deep Learning Compiler Optimizations with rTasks " OSDI-2020 (a) (b) RAMMER
2020-12-3 · Paper Session 1 OSDI 11 0011 20 Rammer Enabling Holistic Deep Learning Compiler Optimizations with rTasks Lingxiao Ma ( MSRA) Zhiqiang Xie ( MSRA) Zhi Yang () Jilong Xue Youshan Miao Wei Cui Wenxiang Hu
2020-4-11 · Rammer achieves this by proposing several novel hardware neutral and clean abstractions for the computation tasks and the hardware accelerators. These abstractions expose a much richer scheduling space to Rammer which employs several heuristics to explore this space and finds efficient schedules. OSDI. This is an embedded video. Talk and
2020-8-1 · " RAMMER Enabling Holistic Deep Learning Compiler Optimizations with rTasks " OSDI-2020 (a) (b) RAMMER
2020-11-7 · READMENNFusion NNFusion "" . masterOSDI paperartifact0.1 . docker image
AIIR zhuanlan.zhihu OSDI 20 RAMMER (NNFusion) zhuanlan.zhihu AI--Dynamic Shape Compiler zhuanlan.zhihu AI-- zhuanlan.zhihu auto vectorizationpolyhedral
2020-11-6 · Rammer Enabling Holistic Deep Learning Compiler Optimizations with rTasks Lingxiao Ma †♢ Zhiqiang Xie ‡ ♢ Zhi Yang† Jilong Xue Youshan Miao♢ Wei Cui ♢ Wenxiang Hu ♢ Fan Yang Lintao Zhang Lidong Zhou †Peking University ‡ ShanghaiTech University ♢Microsoft Research Equal contribution 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI
OSDI 20 RAMMER (NNFusion) zhuanlan.zhihu MIT Lyken mlsys CNN model CUDA 11 feature
2011-1-12 · Evaluating the OSDI© Score1 The OSDI© is assessed on a scale of 0 to 100 with higher scores representing greater disability. The index demonstrates sensitivity and specificity in distinguishing between normal subjects and patients with dry eye disease. The OSDI© is a valid and reliable instrument for measuring dry eye
Lingxiao Ma Zhiqiang Xie Zhi Yang Jilong Xue Youshan Miao Wei Cui Wenxiang Hu Fan Yang Lintao Zhang and Lidong Zhou. 2020. Rammer Enabling Holistic Deep Learning Compiler Optimizations with rTasks. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20).
2020-12-19 · Paper Session 1 OSDI 1 Rammer Enabling Holistic Deep Learning Compiler Optimizations with rTasks AI
OSDI 20 RAMMER (NNFusion) zhuanlan.zhihu MIT Lyken mlsys CNN model CUDA 11 feature
2017-7-12 · Semantic Scholar profile for Lingxiao Ma with 25 highly influential citations and 14 scientific research papers.
2011-1-12 · Evaluating the OSDI© Score1 The OSDI© is assessed on a scale of 0 to 100 with higher scores representing greater disability. The index demonstrates sensitivity and specificity in distinguishing between normal subjects and patients with dry eye disease. The OSDI© is a valid and reliable instrument for measuring dry eye
AIIR zhuanlan.zhihu OSDI 20 RAMMER (NNFusion) zhuanlan.zhihu AI--Dynamic Shape Compiler zhuanlan.zhihu AI-- zhuanlan.zhihu auto vectorizationpolyhedral
2021-7-20 · In this paper we propose Rammer a DNN compiler design that optimizes the execution of DNN workloads on massively parallel accelerators. Rammer generates an efficient static spatio-temporal schedule for a DNN at compile time to minimize scheduling overhead.
2020-11-4 · RAMMER Enabling Holistic Deep Learning Compiler Optimizations with rTasks Lingxiao Ma† Zhiqiang Xie‡ Zhi Yang† Jilong Xue Youshan Miao Wei Cui Wenxiang Hu Fan Yang Lintao Zhang Lidong Zhou †Peking University ‡ShanghaiTech University Microsoft Research Abstract Performing Deep Neural Network (DNN) computation on hardware accelerators efficiently is challenging.