32 Publications

Mark all

[32]
2019 | Conference Paper | IST-REx-ID: 6673
Alistarh, D.-A., Nadiradze, G., & Koval, N. (2019). Efficiency guarantees for parallel incremental algorithms under relaxed schedulers. In 31st ACM Symposium on Parallelism in Algorithms and Architectures (pp. 145–154). Phoenix, AZ, United States: ACM Press. https://doi.org/10.1145/3323165.3323201
View | DOI
 
[31]
2019 | Conference Paper | IST-REx-ID: 7122
Khirirat, S., Johansson, M., & Alistarh, D.-A. (2019). Gradient compression for communication-limited convex optimization. In 2018 IEEE Conference on Decision and Control. Miami Beach, FL, United States: IEEE. https://doi.org/10.1109/cdc.2018.8619625
View | DOI
 
[30]
2019 | Conference Paper | IST-REx-ID: 7228
Koval, N., Alistarh, D.-A., & Elizarov, R. (2019). Scalable FIFO channels for programming via communicating sequential processes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11725, pp. 317–333). Göttingen, Germany: Springer Nature. https://doi.org/10.1007/978-3-030-29400-7_23
View | DOI
 
[29]
2019 | Conference Poster | IST-REx-ID: 6485
Koval, N., Alistarh, D.-A., & Elizarov, R. (2019). Lock-free channels for programming via communicating sequential processes. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming (pp. 417–418). Washington, NY, United States: ACM Press. https://doi.org/10.1145/3293883.3297000
View | DOI
 
[28]
2019 | Conference Paper | IST-REx-ID: 6676   OA
Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., & Zhu, L. (2019). Why extension-based proofs fail. In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing  - STOC 2019 (pp. 986–996). Phoenix, AZ, United States: ACM Press. https://doi.org/10.1145/3313276.3316407
View | DOI | Download (ext.) | arXiv
 
[27]
2019 | Conference Paper | IST-REx-ID: 7201   OA
Renggli, C., Ashkboos, S., Aghagolzadeh, M., Alistarh, D.-A., & Hoefler, T. (2019). SparCML: High-performance sparse communication for machine learning. In International Conference for High Performance Computing, Networking, Storage and Analysis, SC. Denver, CO, Unites States: ACM. https://doi.org/10.1145/3295500.3356222
View | DOI | Download (ext.) | arXiv
 
[26]
2018 | Conference Paper | IST-REx-ID: 5961
Alistarh, D.-A. (2018). A brief tutorial on distributed and concurrent machine learning. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 487–488). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212798
View | DOI
 
[25]
2018 | Conference Paper | IST-REx-ID: 5966   OA
Alistarh, D.-A., Haider, S. K., Kübler, R., & Nadiradze, G. (2018). The transactional conflict problem. In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18 (pp. 383–392). Vienna, Austria: ACM Press. https://doi.org/10.1145/3210377.3210406
View | DOI | Download (ext.) | arXiv
 
[24]
2018 | Conference Paper | IST-REx-ID: 6558   OA
Alistarh, D.-A., Allen-Zhu, Z., & Li, J. (2018). Byzantine Stochastic Gradient Descent. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. Volume 2018, pp. 4613–4623). Montreal, Canada: Neural Information Processing Systems Foundation.
View | Download (ext.) | arXiv
 
[23]
2018 | Conference Paper | IST-REx-ID: 6589   OA
Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat, S., & Renggli, C. (2018). The convergence of sparsified gradient methods. In Advances in Neural Information Processing Systems 31 (Vol. Volume 2018, pp. 5973–5983). Montreal, Canada: Neural information processing systems.
View | Download (ext.) | arXiv
 
[22]
2018 | Journal Article | IST-REx-ID: 536   OA
Alistarh, D.-A., Aspnes, J., King, V., & Saia, J. (2018). Communication-efficient randomized consensus. Distributed Computing, 31(6), 489–501. https://doi.org/10.1007/s00446-017-0315-1
View | Files available | DOI
 
[21]
2018 | Conference Paper | IST-REx-ID: 5962   OA
Alistarh, D.-A., De Sa, C., & Konstantinov, N. H. (2018). The convergence of stochastic gradient descent in asynchronous shared memory. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 169–178). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212763
View | DOI | Download (ext.) | arXiv
 
[20]
2018 | Conference Paper | IST-REx-ID: 5963   OA
Alistarh, D.-A., Brown, T. A., Kopinsky, J., & Nadiradze, G. (2018). Relaxed schedulers can efficiently parallelize iterative algorithms. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 377–386). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212756
View | DOI | Download (ext.) | arXiv
 
[19]
2018 | Conference Paper | IST-REx-ID: 6031
Stojanov, A., Smith, T. M., Alistarh, D.-A., & Puschel, M. (2018). Fast quantized arithmetic on x86: Trading compute for data movement. In 2018 IEEE International Workshop on Signal Processing Systems (Vol. 2018–October). Cape Town, South Africa: IEEE. https://doi.org/10.1109/SiPS.2018.8598402
View | DOI
 
[18]
2018 | Conference Paper | IST-REx-ID: 7116   OA
Grubic, D., Tam, L., Alistarh, D.-A., & Zhang, C. (2018). Synchronous multi-GPU training for deep learning with low-precision communications: An empirical study. In Proceedings of the 21st International Conference on Extending Database Technology (pp. 145–156). Vienna, Austria: OpenProceedings. https://doi.org/10.5441/002/EDBT.2018.14
View | Files available | DOI
 
[17]
2018 | Conference Paper | IST-REx-ID: 7123   OA
Alistarh, D.-A., Aspnes, J., & Gelashvili, R. (2018). Space-optimal majority in population protocols. In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 2221–2239). New Orleans, LA, United States: ACM. https://doi.org/10.1137/1.9781611975031.144
View | DOI | Download (ext.) | arXiv
 
[16]
2018 | Conference Paper | IST-REx-ID: 5964   OA
Aksenov, V., Alistarh, D.-A., & Kuznetsov, P. (2018). Brief Announcement: Performance prediction for coarse-grained locking. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 411–413). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212785
View | DOI | Download (ext.)
 
[15]
2018 | Journal Article | IST-REx-ID: 6001
Alistarh, D.-A., Leiserson, W., Matveev, A., & Shavit, N. (2018). ThreadScan: Automatic and scalable memory reclamation. ACM Transactions on Parallel Computing, 4(4). https://doi.org/10.1145/3201897
View | Files available | DOI
 
[14]
2018 | Conference Paper | IST-REx-ID: 5965   OA
Alistarh, D.-A., Brown, T. A., Kopinsky, J., Li, J. Z., & Nadiradze, G. (2018). Distributionally linearizable data structures. In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18 (pp. 133–142). Vienna, Austria: ACM Press. https://doi.org/10.1145/3210377.3210411
View | DOI | Download (ext.) | arXiv
 
[13]
2017 | Conference Paper | IST-REx-ID: 787   OA
Alistarh, D.-A., Aspnes, J., Eisenstat, D., Rivest, R., & Gelashvili, R. (2017). Time-space trade-offs in population protocols (pp. 2560–2579). Presented at the SODA: Symposium on Discrete Algorithms, SIAM. https://doi.org/doi.org/10.1137/1.9781611974782.169
View | DOI | Download (ext.)
 
[12]
2017 | Conference Paper | IST-REx-ID: 788   OA
Alistarh, D.-A., Dudek, B., Kosowski, A., Soloveichik, D., & Uznański, P. (2017). Robust detection in leak-prone population protocols (Vol. 10467 LNCS, pp. 155–171). Presented at the DNA Computing and Molecular Programming, Springer. https://doi.org/10.1007/978-3-319-66799-7_11
View | DOI | Download (ext.)
 
[11]
2017 | Conference Paper | IST-REx-ID: 790
Kara, K., Alistarh, D.-A., Alonso, G., Mutlu, O., & Zhang, C. (2017). FPGA-accelerated dense linear machine learning: A precision-convergence trade-off (pp. 160–167). Presented at the FCCM: Field-Programmable Custom Computing Machines, IEEE. https://doi.org/10.1109/FCCM.2017.39
View | DOI
 
[10]
2017 | Conference Paper | IST-REx-ID: 431   OA
Alistarh, D.-A., Grubic, D., Li, J., Tomioka, R., & Vojnović, M. (2017). QSGD: Communication-efficient SGD via gradient quantization and encoding (Vol. 2017, pp. 1710–1721). Presented at the NIPS: Neural Information Processing System, Long Beach, CA, United States: Neural Information Processing Systems Foundation, Inc.
View | Download (ext.)
 
[9]
2017 | Conference Paper | IST-REx-ID: 789
Alistarh, D.-A., Leiserson, W., Matveev, A., & Shavit, N. (2017). Forkscan: Conservative memory reclamation for modern operating systems (pp. 483–498). Presented at the EuroSys: European Conference on Computer Systems, ACM. https://doi.org/10.1145/3064176.3064214
View | DOI
 
[8]
2017 | Conference Paper | IST-REx-ID: 791   OA
Alistarh, D.-A., Kopinsky, J., Li, J., & Nadiradze, G. (2017). The power of choice in priority scheduling. In Proceedings of the ACM Symposium on Principles of Distributed Computing (Vol. Part F129314, pp. 283–292). Washington, WA, USA: ACM. https://doi.org/10.1145/3087801.3087810
View | DOI | Download (ext.)
 
[7]
2017 | Conference Paper | IST-REx-ID: 432   OA
Zhang, H., Li, J., Kara, K., Alistarh, D.-A., Liu, J., & Zhang, C. (2017). ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning. In Proceedings of Machine Learning Research (Vol. 70, pp. 4035–4043). Sydney, Australia: PMLR.
View | Files available
 
[6]
2017 | Conference Paper | IST-REx-ID: 487
Baig, G., Radunovic, B., Alistarh, D.-A., Balkwill, M., Karagiannis, T., & Qiu, L. (2017). Towards unlicensed cellular networks in TV white spaces. In Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies (pp. 2–14). Incheon, South Korea: ACM. https://doi.org/10.1145/3143361.3143367
View | DOI
 
[5]
2016 | Conference Paper | IST-REx-ID: 785
Haider, S., Hasenplaugh, W., & Alistarh, D.-A. (2016). Lease/Release: Architectural support for scaling contended data structures (Vol. 12-16-March-2016). Presented at the PPoPP: Principles and Practice of Parallel Pogramming, ACM. https://doi.org/10.1145/2851141.2851155
View | DOI
 
[4]
2016 | Journal Article | IST-REx-ID: 786   OA
Alistarh, D.-A., Censor Hillel, K., & Shavit, N. (2016). Are lock free concurrent algorithms practically wait free . Journal of the ACM, 63(4). https://doi.org/10.1145/2903136
View | DOI | Download (ext.) | arXiv
 
[3]
2015 | Conference Paper | IST-REx-ID: 784
Alistarh, D.-A., Ballani, H., Costa, P., Funnell, A., Benjamin, J., Watts, P., & Thomsen, B. (2015). A high-radix, low-latency optical switch for data centers (pp. 367–368). Presented at the SIGCOMM: Special Interest Group on Data Communication, London, United Kindgdom: ACM. https://doi.org/10.1145/2785956.2790035
View | DOI
 
[2]
2015 | Conference Paper | IST-REx-ID: 778   OA
Alistarh, D.-A., Kopinsky, J., Kuznetsov, P., Ravi, S., & Shavit, N. (2015). Inherent limitations of hybrid transactional memory (Vol. 9363, pp. 185–199). Presented at the DISC: Distributed Computing, Springer. https://doi.org/10.1007/978-3-662-48653-5_13
View | DOI | Download (ext.) | arXiv
 
[1]
2015 | Conference Paper | IST-REx-ID: 780   OA
Alistarh, D.-A., & Gelashvili, R. (2015). Polylogarithmic-time leader election in population protocols (Vol. 9135, pp. 479–491). Presented at the ICALP: International Colloquium on Automota, Languages and Programming, Springer. https://doi.org/10.1007/978-3-662-47666-6_38
View | DOI | Download (ext.) | arXiv
 

Search

Filter Publications

Display / Sort

Citation Style: APA

Export / Embed

32 Publications

Mark all

[32]
2019 | Conference Paper | IST-REx-ID: 6673
Alistarh, D.-A., Nadiradze, G., & Koval, N. (2019). Efficiency guarantees for parallel incremental algorithms under relaxed schedulers. In 31st ACM Symposium on Parallelism in Algorithms and Architectures (pp. 145–154). Phoenix, AZ, United States: ACM Press. https://doi.org/10.1145/3323165.3323201
View | DOI
 
[31]
2019 | Conference Paper | IST-REx-ID: 7122
Khirirat, S., Johansson, M., & Alistarh, D.-A. (2019). Gradient compression for communication-limited convex optimization. In 2018 IEEE Conference on Decision and Control. Miami Beach, FL, United States: IEEE. https://doi.org/10.1109/cdc.2018.8619625
View | DOI
 
[30]
2019 | Conference Paper | IST-REx-ID: 7228
Koval, N., Alistarh, D.-A., & Elizarov, R. (2019). Scalable FIFO channels for programming via communicating sequential processes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11725, pp. 317–333). Göttingen, Germany: Springer Nature. https://doi.org/10.1007/978-3-030-29400-7_23
View | DOI
 
[29]
2019 | Conference Poster | IST-REx-ID: 6485
Koval, N., Alistarh, D.-A., & Elizarov, R. (2019). Lock-free channels for programming via communicating sequential processes. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming (pp. 417–418). Washington, NY, United States: ACM Press. https://doi.org/10.1145/3293883.3297000
View | DOI
 
[28]
2019 | Conference Paper | IST-REx-ID: 6676   OA
Alistarh, D.-A., Aspnes, J., Ellen, F., Gelashvili, R., & Zhu, L. (2019). Why extension-based proofs fail. In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing  - STOC 2019 (pp. 986–996). Phoenix, AZ, United States: ACM Press. https://doi.org/10.1145/3313276.3316407
View | DOI | Download (ext.) | arXiv
 
[27]
2019 | Conference Paper | IST-REx-ID: 7201   OA
Renggli, C., Ashkboos, S., Aghagolzadeh, M., Alistarh, D.-A., & Hoefler, T. (2019). SparCML: High-performance sparse communication for machine learning. In International Conference for High Performance Computing, Networking, Storage and Analysis, SC. Denver, CO, Unites States: ACM. https://doi.org/10.1145/3295500.3356222
View | DOI | Download (ext.) | arXiv
 
[26]
2018 | Conference Paper | IST-REx-ID: 5961
Alistarh, D.-A. (2018). A brief tutorial on distributed and concurrent machine learning. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 487–488). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212798
View | DOI
 
[25]
2018 | Conference Paper | IST-REx-ID: 5966   OA
Alistarh, D.-A., Haider, S. K., Kübler, R., & Nadiradze, G. (2018). The transactional conflict problem. In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18 (pp. 383–392). Vienna, Austria: ACM Press. https://doi.org/10.1145/3210377.3210406
View | DOI | Download (ext.) | arXiv
 
[24]
2018 | Conference Paper | IST-REx-ID: 6558   OA
Alistarh, D.-A., Allen-Zhu, Z., & Li, J. (2018). Byzantine Stochastic Gradient Descent. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. Volume 2018, pp. 4613–4623). Montreal, Canada: Neural Information Processing Systems Foundation.
View | Download (ext.) | arXiv
 
[23]
2018 | Conference Paper | IST-REx-ID: 6589   OA
Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat, S., & Renggli, C. (2018). The convergence of sparsified gradient methods. In Advances in Neural Information Processing Systems 31 (Vol. Volume 2018, pp. 5973–5983). Montreal, Canada: Neural information processing systems.
View | Download (ext.) | arXiv
 
[22]
2018 | Journal Article | IST-REx-ID: 536   OA
Alistarh, D.-A., Aspnes, J., King, V., & Saia, J. (2018). Communication-efficient randomized consensus. Distributed Computing, 31(6), 489–501. https://doi.org/10.1007/s00446-017-0315-1
View | Files available | DOI
 
[21]
2018 | Conference Paper | IST-REx-ID: 5962   OA
Alistarh, D.-A., De Sa, C., & Konstantinov, N. H. (2018). The convergence of stochastic gradient descent in asynchronous shared memory. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 169–178). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212763
View | DOI | Download (ext.) | arXiv
 
[20]
2018 | Conference Paper | IST-REx-ID: 5963   OA
Alistarh, D.-A., Brown, T. A., Kopinsky, J., & Nadiradze, G. (2018). Relaxed schedulers can efficiently parallelize iterative algorithms. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 377–386). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212756
View | DOI | Download (ext.) | arXiv
 
[19]
2018 | Conference Paper | IST-REx-ID: 6031
Stojanov, A., Smith, T. M., Alistarh, D.-A., & Puschel, M. (2018). Fast quantized arithmetic on x86: Trading compute for data movement. In 2018 IEEE International Workshop on Signal Processing Systems (Vol. 2018–October). Cape Town, South Africa: IEEE. https://doi.org/10.1109/SiPS.2018.8598402
View | DOI
 
[18]
2018 | Conference Paper | IST-REx-ID: 7116   OA
Grubic, D., Tam, L., Alistarh, D.-A., & Zhang, C. (2018). Synchronous multi-GPU training for deep learning with low-precision communications: An empirical study. In Proceedings of the 21st International Conference on Extending Database Technology (pp. 145–156). Vienna, Austria: OpenProceedings. https://doi.org/10.5441/002/EDBT.2018.14
View | Files available | DOI
 
[17]
2018 | Conference Paper | IST-REx-ID: 7123   OA
Alistarh, D.-A., Aspnes, J., & Gelashvili, R. (2018). Space-optimal majority in population protocols. In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 2221–2239). New Orleans, LA, United States: ACM. https://doi.org/10.1137/1.9781611975031.144
View | DOI | Download (ext.) | arXiv
 
[16]
2018 | Conference Paper | IST-REx-ID: 5964   OA
Aksenov, V., Alistarh, D.-A., & Kuznetsov, P. (2018). Brief Announcement: Performance prediction for coarse-grained locking. In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18 (pp. 411–413). Egham, United Kingdom: ACM Press. https://doi.org/10.1145/3212734.3212785
View | DOI | Download (ext.)
 
[15]
2018 | Journal Article | IST-REx-ID: 6001
Alistarh, D.-A., Leiserson, W., Matveev, A., & Shavit, N. (2018). ThreadScan: Automatic and scalable memory reclamation. ACM Transactions on Parallel Computing, 4(4). https://doi.org/10.1145/3201897
View | Files available | DOI
 
[14]
2018 | Conference Paper | IST-REx-ID: 5965   OA
Alistarh, D.-A., Brown, T. A., Kopinsky, J., Li, J. Z., & Nadiradze, G. (2018). Distributionally linearizable data structures. In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18 (pp. 133–142). Vienna, Austria: ACM Press. https://doi.org/10.1145/3210377.3210411
View | DOI | Download (ext.) | arXiv
 
[13]
2017 | Conference Paper | IST-REx-ID: 787   OA
Alistarh, D.-A., Aspnes, J., Eisenstat, D., Rivest, R., & Gelashvili, R. (2017). Time-space trade-offs in population protocols (pp. 2560–2579). Presented at the SODA: Symposium on Discrete Algorithms, SIAM. https://doi.org/doi.org/10.1137/1.9781611974782.169
View | DOI | Download (ext.)
 
[12]
2017 | Conference Paper | IST-REx-ID: 788   OA
Alistarh, D.-A., Dudek, B., Kosowski, A., Soloveichik, D., & Uznański, P. (2017). Robust detection in leak-prone population protocols (Vol. 10467 LNCS, pp. 155–171). Presented at the DNA Computing and Molecular Programming, Springer. https://doi.org/10.1007/978-3-319-66799-7_11
View | DOI | Download (ext.)
 
[11]
2017 | Conference Paper | IST-REx-ID: 790
Kara, K., Alistarh, D.-A., Alonso, G., Mutlu, O., & Zhang, C. (2017). FPGA-accelerated dense linear machine learning: A precision-convergence trade-off (pp. 160–167). Presented at the FCCM: Field-Programmable Custom Computing Machines, IEEE. https://doi.org/10.1109/FCCM.2017.39
View | DOI
 
[10]
2017 | Conference Paper | IST-REx-ID: 431   OA
Alistarh, D.-A., Grubic, D., Li, J., Tomioka, R., & Vojnović, M. (2017). QSGD: Communication-efficient SGD via gradient quantization and encoding (Vol. 2017, pp. 1710–1721). Presented at the NIPS: Neural Information Processing System, Long Beach, CA, United States: Neural Information Processing Systems Foundation, Inc.
View | Download (ext.)
 
[9]
2017 | Conference Paper | IST-REx-ID: 789
Alistarh, D.-A., Leiserson, W., Matveev, A., & Shavit, N. (2017). Forkscan: Conservative memory reclamation for modern operating systems (pp. 483–498). Presented at the EuroSys: European Conference on Computer Systems, ACM. https://doi.org/10.1145/3064176.3064214
View | DOI
 
[8]
2017 | Conference Paper | IST-REx-ID: 791   OA
Alistarh, D.-A., Kopinsky, J., Li, J., & Nadiradze, G. (2017). The power of choice in priority scheduling. In Proceedings of the ACM Symposium on Principles of Distributed Computing (Vol. Part F129314, pp. 283–292). Washington, WA, USA: ACM. https://doi.org/10.1145/3087801.3087810
View | DOI | Download (ext.)
 
[7]
2017 | Conference Paper | IST-REx-ID: 432   OA
Zhang, H., Li, J., Kara, K., Alistarh, D.-A., Liu, J., & Zhang, C. (2017). ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning. In Proceedings of Machine Learning Research (Vol. 70, pp. 4035–4043). Sydney, Australia: PMLR.
View | Files available
 
[6]
2017 | Conference Paper | IST-REx-ID: 487
Baig, G., Radunovic, B., Alistarh, D.-A., Balkwill, M., Karagiannis, T., & Qiu, L. (2017). Towards unlicensed cellular networks in TV white spaces. In Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies (pp. 2–14). Incheon, South Korea: ACM. https://doi.org/10.1145/3143361.3143367
View | DOI
 
[5]
2016 | Conference Paper | IST-REx-ID: 785
Haider, S., Hasenplaugh, W., & Alistarh, D.-A. (2016). Lease/Release: Architectural support for scaling contended data structures (Vol. 12-16-March-2016). Presented at the PPoPP: Principles and Practice of Parallel Pogramming, ACM. https://doi.org/10.1145/2851141.2851155
View | DOI
 
[4]
2016 | Journal Article | IST-REx-ID: 786   OA
Alistarh, D.-A., Censor Hillel, K., & Shavit, N. (2016). Are lock free concurrent algorithms practically wait free . Journal of the ACM, 63(4). https://doi.org/10.1145/2903136
View | DOI | Download (ext.) | arXiv
 
[3]
2015 | Conference Paper | IST-REx-ID: 784
Alistarh, D.-A., Ballani, H., Costa, P., Funnell, A., Benjamin, J., Watts, P., & Thomsen, B. (2015). A high-radix, low-latency optical switch for data centers (pp. 367–368). Presented at the SIGCOMM: Special Interest Group on Data Communication, London, United Kindgdom: ACM. https://doi.org/10.1145/2785956.2790035
View | DOI
 
[2]
2015 | Conference Paper | IST-REx-ID: 778   OA
Alistarh, D.-A., Kopinsky, J., Kuznetsov, P., Ravi, S., & Shavit, N. (2015). Inherent limitations of hybrid transactional memory (Vol. 9363, pp. 185–199). Presented at the DISC: Distributed Computing, Springer. https://doi.org/10.1007/978-3-662-48653-5_13
View | DOI | Download (ext.) | arXiv
 
[1]
2015 | Conference Paper | IST-REx-ID: 780   OA
Alistarh, D.-A., & Gelashvili, R. (2015). Polylogarithmic-time leader election in population protocols (Vol. 9135, pp. 479–491). Presented at the ICALP: International Colloquium on Automota, Languages and Programming, Springer. https://doi.org/10.1007/978-3-662-47666-6_38
View | DOI | Download (ext.) | arXiv
 

Search

Filter Publications

Display / Sort

Citation Style: APA

Export / Embed