| 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 | 4522 | 2005 |
| 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 | 329 | 2011 |
| 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 | 263 | 2015 |
| 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 | 228 | 2018 |
| Analyzing parallel programs with pin M Bach, M Charney, R Cohn, E Demikhovsky, T Devor, K Hazelwood, ... Computer 43 (3), 34-41, 2010 | 140 | 2010 |
| Superpin: Parallelizing dynamic instrumentation for real-time performance S Wallace, K Hazelwood International Symposium on Code Generation and Optimization (CGO'07), 209-220, 2007 | 114 | 2007 |
| Fine-Grained Resource Sharing for Concurrent {GPGPU} Kernels C Gregg, J Dorn, K Hazelwood, K Skadron Presented as part of the 4th {USENIX} Workshop on Hot Topics in Parallelism, 2012 | 102 | 2012 |
| 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 | 101 | 2019 |
| Enabling task parallelism in the cuda scheduler M Guevara, C Gregg, K Hazelwood, K Skadron Workshop on Programming Models for Emerging Architectures 9, 2009 | 99 | 2009 |
| A dynamic binary instrumentation engine for the ARM architecture K Hazelwood, A Klauser Proceedings of the 2006 international conference on Compilers, architecture …, 2006 | 93 | 2006 |
| Adaptive online context-sensitive inlining K Hazelwood, D Grove International Symposium on Code Generation and Optimization, 2003. CGO 2003 …, 2003 | 79 | 2003 |
| 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 | 77 | 2018 |
| Improving region selection in dynamic optimization systems D Hiniker, K Hazelwood, MD Smith 38th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO'05 …, 2005 | 64 | 2005 |
| 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 | 62 | 2014 |
| Dynamic heterogeneous scheduling decisions using historical runtime data C Gregg, M Boyer, K Hazelwood, K Skadron Workshop on Applications for Multi-and Many-Core Processors (A4MMC), 1-12, 2011 | 62 | 2011 |
| Code cache management schemes for dynamic optimizers K Hazelwood, MD Smith Proceedings Sixth Annual Workshop on Interaction between Compilers and …, 2002 | 57 | 2002 |
| Eliminating voltage emergencies via microarchitectural voltage control feedback and dynamic optimization K Hazelwood, D Brooks Proceedings of the 2004 international symposium on Low power electronics and …, 2004 | 56 | 2004 |
| Exploring code cache eviction granularities in dynamic optimization systems K Hazelwood, JE Smith International Symposium on Code Generation and Optimization, 2004. CGO 2004 …, 2004 | 54 | 2004 |
| Generational cache management of code traces in dynamic optimization systems K Hazelwood, MD Smith Proceedings. 36th Annual IEEE/ACM International Symposium on …, 2003 | 54 | 2003 |
| 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 | 53 | 2018 |
Robert CohnIntelVerifierad e-postadress på intel.com
