Kim Hazelwood
Cited by
Cited by
Pin: building customized program analysis tools with dynamic instrumentation
CK Luk, R Cohn, R Muth, H Patil, A Klauser, G Lowney, S Wallace, ...
Acm sigplan notices 40 (6), 190-200, 2005
Applied machine learning at facebook: A datacenter infrastructure perspective
K Hazelwood, S Bird, D Brooks, S Chintala, U Diril, D Dzhulgakov, ...
2018 IEEE International Symposium on High Performance Computer Architecture …, 2018
Profiling a warehouse-scale computer
S Kanev, JP Darago, K Hazelwood, P Ranganathan, T Moseley, GY Wei, ...
Proceedings of the 42nd Annual International Symposium on Computer …, 2015
Machine learning at facebook: Understanding inference at the edge
CJ Wu, D Brooks, K Chen, D Chen, S Choudhury, M Dukhan, ...
2019 IEEE international symposium on high performance computer architecture …, 2019
Where is the data? Why you cannot debate CPU vs. GPU performance without the answer
C Gregg, K Hazelwood
(IEEE ISPASS) IEEE International Symposium on Performance Analysis of …, 2011
Mlperf training benchmark
P Mattson, C Cheng, G Diamos, C Coleman, P Micikevicius, D Patterson, ...
Proceedings of Machine Learning and Systems 2, 336-349, 2020
Sustainable ai: Environmental implications, challenges and opportunities
CJ Wu, R Raghavendra, U Gupta, B Acun, N Ardalani, K Maeng, G Chang, ...
Proceedings of Machine Learning and Systems 4, 795-813, 2022
The architectural implications of facebook's dnn-based personalized recommendation
U Gupta, CJ Wu, X Wang, M Naumov, B Reagen, D Brooks, B Cottel, ...
2020 IEEE International Symposium on High Performance Computer Architecture …, 2020
Deep learning inference in facebook data centers: Characterization, performance optimizations and hardware implications
J Park, M Naumov, P Basu, S Deng, A Kalaiah, D Khudia, J Law, P Malani, ...
arXiv preprint arXiv:1811.09886, 2018
Recnmp: Accelerating personalized recommendation with near-memory processing
L Ke, U Gupta, BY Cho, D Brooks, V Chandra, U Diril, A Firoozshahian, ...
2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020
Reducing DRAM footprint with NVM in Facebook
A Eisenman, D Gardner, I AbdelRahman, J Axboe, S Dong, K Hazelwood, ...
Proceedings of the Thirteenth EuroSys Conference, 1-13, 2018
Analyzing parallel programs with pin
M Bach, M Charney, R Cohn, E Demikhovsky, T Devor, K Hazelwood, ...
Computer 43 (3), 34-41, 2010
{Fine-Grained} Resource Sharing for Concurrent {GPGPU} Kernels
C Gregg, J Dorn, K Hazelwood, K Skadron
4th USENIX Workshop on Hot Topics in Parallelism (HotPar 12), 2012
Enabling task parallelism in the cuda scheduler
M Guevara, C Gregg, K Hazelwood, K Skadron
Workshop on Programming Models for Emerging Architectures 9, 84, 2009
Superpin: Parallelizing dynamic instrumentation for real-time performance
S Wallace, K Hazelwood
International Symposium on Code Generation and Optimization (CGO'07), 209-220, 2007
A dynamic binary instrumentation engine for the arm architecture
K Hazelwood, A Klauser
Proceedings of the 2006 international conference on Compilers, architecture …, 2006
Tradeoffs between power management and tail latency in warehouse-scale applications
S Kanev, K Hazelwood, GY Wei, D Brooks
2014 IEEE International Symposium on Workload Characterization (IISWC), 31-40, 2014
Adaptive online context-sensitive inlining
K Hazelwood, D Grove
International Symposium on Code Generation and Optimization, 2003. CGO 2003 …, 2003
Understanding training efficiency of deep learning recommendation models at scale
B Acun, M Murphy, X Wang, J Nie, CJ Wu, K Hazelwood
2021 IEEE International Symposium on High-Performance Computer Architecture …, 2021
Bandana: Using non-volatile memory for storing deep learning models
A Eisenman, M Naumov, D Gardner, M Smelyanskiy, S Pupyrev, ...
Proceedings of machine learning and systems 1, 40-52, 2019
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