38 Publications

Mark all

[38]
2020 | Conference Paper | IST-REx-ID: 7635
Koval, Nikita, Mariia Sokolova, Alexander Fedorov, Dan-Adrian Alistarh, and Dmitry Tsitelov. “Testing Concurrency on the JVM with Lincheck.” In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 423–24. ACM, 2020. https://doi.org/10.1145/3332466.3374503.
View | DOI
 
[37]
2020 | Conference Paper | IST-REx-ID: 7605   OA
Alistarh, Dan-Adrian, Alexander Fedorov, and Nikita Koval. “In Search of the Fastest Concurrent Union-Find Algorithm,” 153:15. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020. https://doi.org/10.4230/LIPIcs.OPODIS.2019.15.
View | Files available | DOI | arXiv
 
[36]
2020 | Conference Paper | IST-REx-ID: 7636
Brown, Trevor A, Aleksandar Prokopec, and Dan-Adrian Alistarh. “Non-Blocking Interpolation Search Trees with Doubly-Logarithmic Running Time.” In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 276–91. ACM, 2020. https://doi.org/10.1145/3332466.3374542.
View | DOI
 
[35]
2019 | Conference Paper | IST-REx-ID: 6673   OA
Alistarh, Dan-Adrian, Giorgi Nadiradze, and Nikita Koval. “Efficiency Guarantees for Parallel Incremental Algorithms under Relaxed Schedulers.” In 31st ACM Symposium on Parallelism in Algorithms and Architectures, 145–54. ACM Press, 2019. https://doi.org/10.1145/3323165.3323201.
View | DOI | Download (ext.) | arXiv
 
[34]
2019 | Conference Paper | IST-REx-ID: 7122
Khirirat, Sarit, Mikael Johansson, and Dan-Adrian Alistarh. “Gradient Compression for Communication-Limited Convex Optimization.” In 2018 IEEE Conference on Decision and Control. IEEE, 2019. https://doi.org/10.1109/cdc.2018.8619625.
View | DOI
 
[33]
2019 | Conference Paper | IST-REx-ID: 7228
Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. “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), 11725:317–33. Springer Nature, 2019. https://doi.org/10.1007/978-3-030-29400-7_23.
View | DOI
 
[32]
2019 | Conference Paper | IST-REx-ID: 7437   OA
Yu, Chen, Hanlin Tang, Cedric Renggli, Simon Kassing, Ankit Singla, Dan-Adrian Alistarh, Ce Zhang, and Ji Liu. “Distributed Learning over Unreliable Networks.” In 36th International Conference on Machine Learning, ICML 2019, 2019–June:12481–512. IMLS, 2019.
View | Download (ext.) | arXiv
 
[31]
2019 | Conference Poster | IST-REx-ID: 6485
Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. Lock-Free Channels for Programming via Communicating Sequential Processes. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming. ACM Press, 2019. https://doi.org/10.1145/3293883.3297000.
View | DOI
 
[30]
2019 | Conference Paper | IST-REx-ID: 7201   OA
Renggli, Cedric, Saleh Ashkboos, Mehdi Aghagolzadeh, Dan-Adrian Alistarh, and Torsten Hoefler. “SparCML: High-Performance Sparse Communication for Machine Learning.” In International Conference for High Performance Computing, Networking, Storage and Analysis, SC. ACM, 2019. https://doi.org/10.1145/3295500.3356222.
View | DOI | Download (ext.) | arXiv
 
[29]
2019 | Conference Paper | IST-REx-ID: 6676   OA
Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi Zhu. “Why Extension-Based Proofs Fail.” In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, 986–96. ACM Press, 2019. https://doi.org/10.1145/3313276.3316407.
View | DOI | Download (ext.) | arXiv
 
[28]
2019 | Conference Paper | IST-REx-ID: 7542   OA
Wendler, Chris, Dan-Adrian Alistarh, and Markus Püschel. “Powerset Convolutional Neural Networks,” 32:927–38. Neural Information Processing Systems Foundation, 2019.
View | Download (ext.) | arXiv
 
[27]
2018 | Conference Paper | IST-REx-ID: 5961
Alistarh, Dan-Adrian. “A Brief Tutorial on Distributed and Concurrent Machine Learning.” In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 487–88. ACM Press, 2018. https://doi.org/10.1145/3212734.3212798.
View | DOI
 
[26]
2018 | Conference Paper | IST-REx-ID: 5966   OA
Alistarh, Dan-Adrian, Syed Kamran Haider, Raphael Kübler, and Giorgi Nadiradze. “The Transactional Conflict Problem.” In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, 383–92. ACM Press, 2018. https://doi.org/10.1145/3210377.3210406.
View | DOI | Download (ext.) | arXiv
 
[25]
2018 | Conference Paper | IST-REx-ID: 6558   OA
Alistarh, Dan-Adrian, Zeyuan Allen-Zhu, and Jerry Li. “Byzantine Stochastic Gradient Descent.” In Advances in Neural Information Processing Systems, edited by S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Volume 2018:4613–23. Neural Information Processing Systems Foundation, 2018.
View | Download (ext.) | arXiv
 
[24]
2018 | Conference Paper | IST-REx-ID: 6589   OA
Alistarh, Dan-Adrian, Torsten Hoefler, Mikael Johansson, Nikola H Konstantinov, Sarit Khirirat, and Cedric Renggli. “The Convergence of Sparsified Gradient Methods.” In Advances in Neural Information Processing Systems 31, Volume 2018:5973–83. Neural information processing systems, 2018.
View | Download (ext.) | arXiv
 
[23]
2018 | Journal Article | IST-REx-ID: 536   OA
Alistarh, Dan-Adrian, James Aspnes, Valerie King, and Jared Saia. “Communication-Efficient Randomized Consensus.” Distributed Computing 31, no. 6 (2018): 489–501. https://doi.org/10.1007/s00446-017-0315-1.
View | Files available | DOI
 
[22]
2018 | Conference Paper | IST-REx-ID: 5962   OA
Alistarh, Dan-Adrian, Christopher De Sa, and Nikola H Konstantinov. “The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory.” In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 169–78. ACM Press, 2018. https://doi.org/10.1145/3212734.3212763.
View | DOI | Download (ext.) | arXiv
 
[21]
2018 | Conference Paper | IST-REx-ID: 7812   OA
Polino, Antonio, Razvan Pascanu, and Dan-Adrian Alistarh. “Model Compression via Distillation and Quantization.” In 6th International Conference on Learning Representations, 2018.
View | Files available | arXiv
 
[20]
2018 | Conference Paper | IST-REx-ID: 5963   OA
Alistarh, Dan-Adrian, Trevor A Brown, Justin Kopinsky, and Giorgi Nadiradze. “Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms.” In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 377–86. ACM Press, 2018. https://doi.org/10.1145/3212734.3212756.
View | DOI | Download (ext.) | arXiv
 
[19]
2018 | Conference Paper | IST-REx-ID: 6031
Stojanov, Alen, Tyler Michael Smith, Dan-Adrian Alistarh, and Markus Puschel. “Fast Quantized Arithmetic on X86: Trading Compute for Data Movement.” In 2018 IEEE International Workshop on Signal Processing Systems, Vol. 2018–October. IEEE, 2018. https://doi.org/10.1109/SiPS.2018.8598402.
View | DOI
 
[18]
2018 | Conference Paper | IST-REx-ID: 7116   OA
Grubic, Demjan, Leo Tam, Dan-Adrian Alistarh, and Ce Zhang. “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, 145–56. OpenProceedings, 2018. https://doi.org/10.5441/002/EDBT.2018.14.
View | Files available | DOI
 
[17]
2018 | Conference Paper | IST-REx-ID: 7123   OA
Alistarh, Dan-Adrian, James Aspnes, and Rati Gelashvili. “Space-Optimal Majority in Population Protocols.” In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, 2221–39. ACM, 2018. https://doi.org/10.1137/1.9781611975031.144.
View | DOI | Download (ext.) | arXiv
 
[16]
2018 | Journal Article | IST-REx-ID: 6001
Alistarh, Dan-Adrian, William Leiserson, Alexander Matveev, and Nir Shavit. “ThreadScan: Automatic and Scalable Memory Reclamation.” ACM Transactions on Parallel Computing 4, no. 4 (2018). https://doi.org/10.1145/3201897.
View | Files available | DOI
 
[15]
2018 | Conference Paper | IST-REx-ID: 5964   OA
Aksenov, Vitaly, Dan-Adrian Alistarh, and Petr Kuznetsov. “Brief Announcement: Performance Prediction for Coarse-Grained Locking.” In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 411–13. ACM Press, 2018. https://doi.org/10.1145/3212734.3212785.
View | DOI | Download (ext.)
 
[14]
2018 | Conference Paper | IST-REx-ID: 5965   OA
Alistarh, Dan-Adrian, Trevor A Brown, Justin Kopinsky, Jerry Z. Li, and Giorgi Nadiradze. “Distributionally Linearizable Data Structures.” In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, 133–42. ACM Press, 2018. https://doi.org/10.1145/3210377.3210411.
View | DOI | Download (ext.) | arXiv
 
[13]
2017 | Conference Paper | IST-REx-ID: 787   OA
Alistarh, Dan-Adrian, James Aspnes, David Eisenstat, Ronald Rivest, and Rati Gelashvili. “Time-Space Trade-Offs in Population Protocols,” 2560–79. SIAM, 2017. https://doi.org/doi.org/10.1137/1.9781611974782.169.
View | DOI | Download (ext.)
 
[12]
2017 | Conference Paper | IST-REx-ID: 788   OA
Alistarh, Dan-Adrian, Bartłomiej Dudek, Adrian Kosowski, David Soloveichik, and Przemysław Uznański. “Robust Detection in Leak-Prone Population Protocols,” 10467 LNCS:155–71. Springer, 2017. https://doi.org/10.1007/978-3-319-66799-7_11.
View | DOI | Download (ext.)
 
[11]
2017 | Conference Paper | IST-REx-ID: 790
Kara, Kaan, Dan-Adrian Alistarh, Gustavo Alonso, Onur Mutlu, and Ce Zhang. “FPGA-Accelerated Dense Linear Machine Learning: A Precision-Convergence Trade-Off,” 160–67. IEEE, 2017. https://doi.org/10.1109/FCCM.2017.39.
View | DOI
 
[10]
2017 | Conference Paper | IST-REx-ID: 431   OA
Alistarh, Dan-Adrian, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnović. “QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding,” 2017:1710–21. Neural Information Processing Systems Foundation, Inc., 2017.
View | Download (ext.)
 
[9]
2017 | Conference Paper | IST-REx-ID: 789
Alistarh, Dan-Adrian, William Leiserson, Alexander Matveev, and Nir Shavit. “Forkscan: Conservative Memory Reclamation for Modern Operating Systems,” 483–98. ACM, 2017. https://doi.org/10.1145/3064176.3064214.
View | DOI
 
[8]
2017 | Conference Paper | IST-REx-ID: 791   OA
Alistarh, Dan-Adrian, Justin Kopinsky, Jerry Li, and Giorgi Nadiradze. “The Power of Choice in Priority Scheduling.” In Proceedings of the ACM Symposium on Principles of Distributed Computing, Part F129314:283–92. ACM, 2017. https://doi.org/10.1145/3087801.3087810.
View | DOI | Download (ext.)
 
[7]
2017 | Conference Paper | IST-REx-ID: 432   OA
Zhang, Hantian, Jerry Li, Kaan Kara, Dan-Adrian Alistarh, Ji Liu, and Ce Zhang. “ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning.” In Proceedings of Machine Learning Research, 70:4035–43. PMLR, 2017.
View | Files available
 
[6]
2017 | Conference Paper | IST-REx-ID: 487
Baig, Ghufran, Bozidar Radunovic, Dan-Adrian Alistarh, Matthew Balkwill, Thomas Karagiannis, and Lili Qiu. “Towards Unlicensed Cellular Networks in TV White Spaces.” In Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies, 2–14. ACM, 2017. https://doi.org/10.1145/3143361.3143367.
View | DOI
 
[5]
2016 | Conference Paper | IST-REx-ID: 785
Haider, Syed, William Hasenplaugh, and Dan-Adrian Alistarh. “Lease/Release: Architectural Support for Scaling Contended Data Structures,” Vol. 12-16-March-2016. ACM, 2016. https://doi.org/10.1145/2851141.2851155.
View | DOI
 
[4]
2016 | Journal Article | IST-REx-ID: 786   OA
Alistarh, Dan-Adrian, Keren Censor Hillel, and Nir Shavit. “Are Lock Free Concurrent Algorithms Practically Wait Free .” Journal of the ACM 63, no. 4 (2016). https://doi.org/10.1145/2903136.
View | DOI | Download (ext.) | arXiv
 
[3]
2015 | Conference Paper | IST-REx-ID: 784
Alistarh, Dan-Adrian, Hitesh Ballani, Paolo Costa, Adam Funnell, Joshua Benjamin, Philip Watts, and Benn Thomsen. “A High-Radix, Low-Latency Optical Switch for Data Centers,” 367–68. ACM, 2015. https://doi.org/10.1145/2785956.2790035.
View | DOI
 
[2]
2015 | Conference Paper | IST-REx-ID: 778   OA
Alistarh, Dan-Adrian, Justin Kopinsky, Petr Kuznetsov, Srivatsan Ravi, and Nir Shavit. “Inherent Limitations of Hybrid Transactional Memory,” 9363:185–99. Springer, 2015. 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, Dan-Adrian, and Rati Gelashvili. “Polylogarithmic-Time Leader Election in Population Protocols,” 9135:479–91. Springer, 2015. https://doi.org/10.1007/978-3-662-47666-6_38.
View | DOI | Download (ext.) | arXiv
 

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38 Publications

Mark all

[38]
2020 | Conference Paper | IST-REx-ID: 7635
Koval, Nikita, Mariia Sokolova, Alexander Fedorov, Dan-Adrian Alistarh, and Dmitry Tsitelov. “Testing Concurrency on the JVM with Lincheck.” In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 423–24. ACM, 2020. https://doi.org/10.1145/3332466.3374503.
View | DOI
 
[37]
2020 | Conference Paper | IST-REx-ID: 7605   OA
Alistarh, Dan-Adrian, Alexander Fedorov, and Nikita Koval. “In Search of the Fastest Concurrent Union-Find Algorithm,” 153:15. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2020. https://doi.org/10.4230/LIPIcs.OPODIS.2019.15.
View | Files available | DOI | arXiv
 
[36]
2020 | Conference Paper | IST-REx-ID: 7636
Brown, Trevor A, Aleksandar Prokopec, and Dan-Adrian Alistarh. “Non-Blocking Interpolation Search Trees with Doubly-Logarithmic Running Time.” In Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP, 276–91. ACM, 2020. https://doi.org/10.1145/3332466.3374542.
View | DOI
 
[35]
2019 | Conference Paper | IST-REx-ID: 6673   OA
Alistarh, Dan-Adrian, Giorgi Nadiradze, and Nikita Koval. “Efficiency Guarantees for Parallel Incremental Algorithms under Relaxed Schedulers.” In 31st ACM Symposium on Parallelism in Algorithms and Architectures, 145–54. ACM Press, 2019. https://doi.org/10.1145/3323165.3323201.
View | DOI | Download (ext.) | arXiv
 
[34]
2019 | Conference Paper | IST-REx-ID: 7122
Khirirat, Sarit, Mikael Johansson, and Dan-Adrian Alistarh. “Gradient Compression for Communication-Limited Convex Optimization.” In 2018 IEEE Conference on Decision and Control. IEEE, 2019. https://doi.org/10.1109/cdc.2018.8619625.
View | DOI
 
[33]
2019 | Conference Paper | IST-REx-ID: 7228
Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. “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), 11725:317–33. Springer Nature, 2019. https://doi.org/10.1007/978-3-030-29400-7_23.
View | DOI
 
[32]
2019 | Conference Paper | IST-REx-ID: 7437   OA
Yu, Chen, Hanlin Tang, Cedric Renggli, Simon Kassing, Ankit Singla, Dan-Adrian Alistarh, Ce Zhang, and Ji Liu. “Distributed Learning over Unreliable Networks.” In 36th International Conference on Machine Learning, ICML 2019, 2019–June:12481–512. IMLS, 2019.
View | Download (ext.) | arXiv
 
[31]
2019 | Conference Poster | IST-REx-ID: 6485
Koval, Nikita, Dan-Adrian Alistarh, and Roman Elizarov. Lock-Free Channels for Programming via Communicating Sequential Processes. Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming. ACM Press, 2019. https://doi.org/10.1145/3293883.3297000.
View | DOI
 
[30]
2019 | Conference Paper | IST-REx-ID: 7201   OA
Renggli, Cedric, Saleh Ashkboos, Mehdi Aghagolzadeh, Dan-Adrian Alistarh, and Torsten Hoefler. “SparCML: High-Performance Sparse Communication for Machine Learning.” In International Conference for High Performance Computing, Networking, Storage and Analysis, SC. ACM, 2019. https://doi.org/10.1145/3295500.3356222.
View | DOI | Download (ext.) | arXiv
 
[29]
2019 | Conference Paper | IST-REx-ID: 6676   OA
Alistarh, Dan-Adrian, James Aspnes, Faith Ellen, Rati Gelashvili, and Leqi Zhu. “Why Extension-Based Proofs Fail.” In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, 986–96. ACM Press, 2019. https://doi.org/10.1145/3313276.3316407.
View | DOI | Download (ext.) | arXiv
 
[28]
2019 | Conference Paper | IST-REx-ID: 7542   OA
Wendler, Chris, Dan-Adrian Alistarh, and Markus Püschel. “Powerset Convolutional Neural Networks,” 32:927–38. Neural Information Processing Systems Foundation, 2019.
View | Download (ext.) | arXiv
 
[27]
2018 | Conference Paper | IST-REx-ID: 5961
Alistarh, Dan-Adrian. “A Brief Tutorial on Distributed and Concurrent Machine Learning.” In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 487–88. ACM Press, 2018. https://doi.org/10.1145/3212734.3212798.
View | DOI
 
[26]
2018 | Conference Paper | IST-REx-ID: 5966   OA
Alistarh, Dan-Adrian, Syed Kamran Haider, Raphael Kübler, and Giorgi Nadiradze. “The Transactional Conflict Problem.” In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, 383–92. ACM Press, 2018. https://doi.org/10.1145/3210377.3210406.
View | DOI | Download (ext.) | arXiv
 
[25]
2018 | Conference Paper | IST-REx-ID: 6558   OA
Alistarh, Dan-Adrian, Zeyuan Allen-Zhu, and Jerry Li. “Byzantine Stochastic Gradient Descent.” In Advances in Neural Information Processing Systems, edited by S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Volume 2018:4613–23. Neural Information Processing Systems Foundation, 2018.
View | Download (ext.) | arXiv
 
[24]
2018 | Conference Paper | IST-REx-ID: 6589   OA
Alistarh, Dan-Adrian, Torsten Hoefler, Mikael Johansson, Nikola H Konstantinov, Sarit Khirirat, and Cedric Renggli. “The Convergence of Sparsified Gradient Methods.” In Advances in Neural Information Processing Systems 31, Volume 2018:5973–83. Neural information processing systems, 2018.
View | Download (ext.) | arXiv
 
[23]
2018 | Journal Article | IST-REx-ID: 536   OA
Alistarh, Dan-Adrian, James Aspnes, Valerie King, and Jared Saia. “Communication-Efficient Randomized Consensus.” Distributed Computing 31, no. 6 (2018): 489–501. https://doi.org/10.1007/s00446-017-0315-1.
View | Files available | DOI
 
[22]
2018 | Conference Paper | IST-REx-ID: 5962   OA
Alistarh, Dan-Adrian, Christopher De Sa, and Nikola H Konstantinov. “The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory.” In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 169–78. ACM Press, 2018. https://doi.org/10.1145/3212734.3212763.
View | DOI | Download (ext.) | arXiv
 
[21]
2018 | Conference Paper | IST-REx-ID: 7812   OA
Polino, Antonio, Razvan Pascanu, and Dan-Adrian Alistarh. “Model Compression via Distillation and Quantization.” In 6th International Conference on Learning Representations, 2018.
View | Files available | arXiv
 
[20]
2018 | Conference Paper | IST-REx-ID: 5963   OA
Alistarh, Dan-Adrian, Trevor A Brown, Justin Kopinsky, and Giorgi Nadiradze. “Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms.” In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 377–86. ACM Press, 2018. https://doi.org/10.1145/3212734.3212756.
View | DOI | Download (ext.) | arXiv
 
[19]
2018 | Conference Paper | IST-REx-ID: 6031
Stojanov, Alen, Tyler Michael Smith, Dan-Adrian Alistarh, and Markus Puschel. “Fast Quantized Arithmetic on X86: Trading Compute for Data Movement.” In 2018 IEEE International Workshop on Signal Processing Systems, Vol. 2018–October. IEEE, 2018. https://doi.org/10.1109/SiPS.2018.8598402.
View | DOI
 
[18]
2018 | Conference Paper | IST-REx-ID: 7116   OA
Grubic, Demjan, Leo Tam, Dan-Adrian Alistarh, and Ce Zhang. “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, 145–56. OpenProceedings, 2018. https://doi.org/10.5441/002/EDBT.2018.14.
View | Files available | DOI
 
[17]
2018 | Conference Paper | IST-REx-ID: 7123   OA
Alistarh, Dan-Adrian, James Aspnes, and Rati Gelashvili. “Space-Optimal Majority in Population Protocols.” In Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, 2221–39. ACM, 2018. https://doi.org/10.1137/1.9781611975031.144.
View | DOI | Download (ext.) | arXiv
 
[16]
2018 | Journal Article | IST-REx-ID: 6001
Alistarh, Dan-Adrian, William Leiserson, Alexander Matveev, and Nir Shavit. “ThreadScan: Automatic and Scalable Memory Reclamation.” ACM Transactions on Parallel Computing 4, no. 4 (2018). https://doi.org/10.1145/3201897.
View | Files available | DOI
 
[15]
2018 | Conference Paper | IST-REx-ID: 5964   OA
Aksenov, Vitaly, Dan-Adrian Alistarh, and Petr Kuznetsov. “Brief Announcement: Performance Prediction for Coarse-Grained Locking.” In Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, 411–13. ACM Press, 2018. https://doi.org/10.1145/3212734.3212785.
View | DOI | Download (ext.)
 
[14]
2018 | Conference Paper | IST-REx-ID: 5965   OA
Alistarh, Dan-Adrian, Trevor A Brown, Justin Kopinsky, Jerry Z. Li, and Giorgi Nadiradze. “Distributionally Linearizable Data Structures.” In Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, 133–42. ACM Press, 2018. https://doi.org/10.1145/3210377.3210411.
View | DOI | Download (ext.) | arXiv
 
[13]
2017 | Conference Paper | IST-REx-ID: 787   OA
Alistarh, Dan-Adrian, James Aspnes, David Eisenstat, Ronald Rivest, and Rati Gelashvili. “Time-Space Trade-Offs in Population Protocols,” 2560–79. SIAM, 2017. https://doi.org/doi.org/10.1137/1.9781611974782.169.
View | DOI | Download (ext.)
 
[12]
2017 | Conference Paper | IST-REx-ID: 788   OA
Alistarh, Dan-Adrian, Bartłomiej Dudek, Adrian Kosowski, David Soloveichik, and Przemysław Uznański. “Robust Detection in Leak-Prone Population Protocols,” 10467 LNCS:155–71. Springer, 2017. https://doi.org/10.1007/978-3-319-66799-7_11.
View | DOI | Download (ext.)
 
[11]
2017 | Conference Paper | IST-REx-ID: 790
Kara, Kaan, Dan-Adrian Alistarh, Gustavo Alonso, Onur Mutlu, and Ce Zhang. “FPGA-Accelerated Dense Linear Machine Learning: A Precision-Convergence Trade-Off,” 160–67. IEEE, 2017. https://doi.org/10.1109/FCCM.2017.39.
View | DOI
 
[10]
2017 | Conference Paper | IST-REx-ID: 431   OA
Alistarh, Dan-Adrian, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnović. “QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding,” 2017:1710–21. Neural Information Processing Systems Foundation, Inc., 2017.
View | Download (ext.)
 
[9]
2017 | Conference Paper | IST-REx-ID: 789
Alistarh, Dan-Adrian, William Leiserson, Alexander Matveev, and Nir Shavit. “Forkscan: Conservative Memory Reclamation for Modern Operating Systems,” 483–98. ACM, 2017. https://doi.org/10.1145/3064176.3064214.
View | DOI
 
[8]
2017 | Conference Paper | IST-REx-ID: 791   OA
Alistarh, Dan-Adrian, Justin Kopinsky, Jerry Li, and Giorgi Nadiradze. “The Power of Choice in Priority Scheduling.” In Proceedings of the ACM Symposium on Principles of Distributed Computing, Part F129314:283–92. ACM, 2017. https://doi.org/10.1145/3087801.3087810.
View | DOI | Download (ext.)
 
[7]
2017 | Conference Paper | IST-REx-ID: 432   OA
Zhang, Hantian, Jerry Li, Kaan Kara, Dan-Adrian Alistarh, Ji Liu, and Ce Zhang. “ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning.” In Proceedings of Machine Learning Research, 70:4035–43. PMLR, 2017.
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[6]
2017 | Conference Paper | IST-REx-ID: 487
Baig, Ghufran, Bozidar Radunovic, Dan-Adrian Alistarh, Matthew Balkwill, Thomas Karagiannis, and Lili Qiu. “Towards Unlicensed Cellular Networks in TV White Spaces.” In Proceedings of the 2017 13th International Conference on Emerging Networking EXperiments and Technologies, 2–14. ACM, 2017. https://doi.org/10.1145/3143361.3143367.
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[5]
2016 | Conference Paper | IST-REx-ID: 785
Haider, Syed, William Hasenplaugh, and Dan-Adrian Alistarh. “Lease/Release: Architectural Support for Scaling Contended Data Structures,” Vol. 12-16-March-2016. ACM, 2016. https://doi.org/10.1145/2851141.2851155.
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[4]
2016 | Journal Article | IST-REx-ID: 786   OA
Alistarh, Dan-Adrian, Keren Censor Hillel, and Nir Shavit. “Are Lock Free Concurrent Algorithms Practically Wait Free .” Journal of the ACM 63, no. 4 (2016). https://doi.org/10.1145/2903136.
View | DOI | Download (ext.) | arXiv
 
[3]
2015 | Conference Paper | IST-REx-ID: 784
Alistarh, Dan-Adrian, Hitesh Ballani, Paolo Costa, Adam Funnell, Joshua Benjamin, Philip Watts, and Benn Thomsen. “A High-Radix, Low-Latency Optical Switch for Data Centers,” 367–68. ACM, 2015. https://doi.org/10.1145/2785956.2790035.
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[2]
2015 | Conference Paper | IST-REx-ID: 778   OA
Alistarh, Dan-Adrian, Justin Kopinsky, Petr Kuznetsov, Srivatsan Ravi, and Nir Shavit. “Inherent Limitations of Hybrid Transactional Memory,” 9363:185–99. Springer, 2015. 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, Dan-Adrian, and Rati Gelashvili. “Polylogarithmic-Time Leader Election in Population Protocols,” 9135:479–91. Springer, 2015. https://doi.org/10.1007/978-3-662-47666-6_38.
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