Olatunji Ruwase
Olatunji Ruwase
Microsoft Research
Verified email at microsoft.com - Homepage
Cited by
Cited by
A Practical Dynamic Buffer Overflow Detector.
O Ruwase, MS Lam
NDSS 2004, 159-169, 2004
Accelerating deep convolutional neural networks using specialized hardware
K Ovtcharov, O Ruwase, JY Kim, J Fowers, K Strauss, ES Chung
Microsoft Research Whitepaper 2 (11), 1-4, 2015
Flexible hardware acceleration for instruction-grain program monitoring
S Chen, M Kozuch, T Strigkos, B Falsafi, PB Gibbons, TC Mowry, ...
ACM SIGARCH Computer Architecture News 36 (3), 377-388, 2008
Parallelizing dynamic information flow tracking
O Ruwase, PB Gibbons, TC Mowry, V Ramachandran, S Chen, M Kozuch, ...
Proceedings of the twentieth annual symposium on Parallelism in algorithms …, 2008
Ditto: a system for opportunistic caching in multi-hop wireless networks
FR Dogar, A Phanishayee, H Pucha, O Ruwase, DG Andersen
Proceedings of the 14th ACM international conference on Mobile computing and …, 2008
Toward accelerating deep learning at scale using specialized hardware in the datacenter
K Ovtcharov, O Ruwase, JY Kim, J Fowers, K Strauss, ES Chung
2015 IEEE Hot Chips 27 Symposium (HCS), 1-38, 2015
Zero: Memory optimizations toward training trillion parameter models
S Rajbhandari, J Rasley, O Ruwase, Y He
SC20: International Conference for High Performance Computing, Networking …, 2020
Performance modeling and scalability optimization of distributed deep learning systems
F Yan, O Ruwase, Y He, T Chilimbi
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015
Page overlays: An enhanced virtual memory framework to enable fine-grained memory management
V Seshadri, G Pekhimenko, O Ruwase, O Mutlu, PB Gibbons, MA Kozuch, ...
ACM SIGARCH Computer Architecture News 43 (3S), 79-91, 2015
Hyperdrive: Exploring hyperparameters with pop scheduling
J Rasley, Y He, F Yan, O Ruwase, R Fonseca
Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference, 1-13, 2017
Decoupled lifeguards: enabling path optimizations for dynamic correctness checking tools
O Ruwase, S Chen, PB Gibbons, TC Mowry
Proceedings of the 2010 ACM SIGPLAN conference on Programming language …, 2010
Flexible hardware acceleration for instruction-grain lifeguards
S Chen, M Kozuch, PB Gibbons, M Ryan, T Strigkos, TC Mowry, ...
IEEE micro 29 (1), 62-72, 2009
SERF: efficient scheduling for fast deep neural network serving via judicious parallelism
F Yan, O Ruwase, Y He, E Smirni
SC'16: Proceedings of the International Conference for High Performance …, 2016
Efficient deep neural network serving: Fast and furious
F Yan, Y He, O Ruwase, E Smirni
IEEE Transactions on Network and Service Management 15 (1), 112-126, 2018
Optimizing cnns on multicores for scalability, performance and goodput
S Rajbhandari, Y He, O Ruwase, M Carbin, T Chilimbi
ACM SIGARCH Computer Architecture News 45 (1), 267-280, 2017
Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
J Rasley, S Rajbhandari, O Ruwase, Y He
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
Guardrail: a high fidelity approach to protecting hardware devices from buggy drivers
O Ruwase, MA Kozuch, PB Gibbons, T Mowry
Architectural support for programming languages and operating systems, Pages …, 2014
Lstm-sharp: An adaptable, energy-efficient hardware accelerator for long short-term memory
R Yazdani, O Ruwase, M Zhang, Y He, JM Arnau, A González
arXiv preprint arXiv:1911.01258, 2019
Tool for investigating the performance of a distributed processing system
T Chilimbi, Y Suzue, JT Apacible, K Kalyanaraman, O Ruwase, Y He, ...
US Patent 10,686,869, 2020
Accelerating large scale deep learning inference through DeepCPU at microsoft
M Zhang, S Rajbandari, W Wang, E Zheng, O Ruwase, J Rasley, J Li, ...
2019 {USENIX} Conference on Operational Machine Learning (OpML 19), 5-7, 2019
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