Dan-Adrian Alistarh

Alistarh Group

33 Publications

Mark all

[33]
2019 | Conference Paper | IST-REx-ID: 6673
D.-A. Alistarh, G. Nadiradze, and N. Koval, “Efficiency guarantees for parallel incremental algorithms under relaxed schedulers,” in 31st ACM Symposium on Parallelism in Algorithms and Architectures, Phoenix, AZ, United States, 2019, pp. 145–154.
View | DOI
 
[32]
2019 | Conference Paper | IST-REx-ID: 7122
S. Khirirat, M. Johansson, and D.-A. Alistarh, “Gradient compression for communication-limited convex optimization,” in 2018 IEEE Conference on Decision and Control, Miami Beach, FL, United States, 2019.
View | DOI
 
[31]
2019 | Conference Paper | IST-REx-ID: 7228
N. Koval, D.-A. Alistarh, and R. 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), Göttingen, Germany, 2019, vol. 11725, pp. 317–333.
View | DOI
 
[30]
2019 | Conference Paper | IST-REx-ID: 7437   OA
C. Yu et al., “Distributed learning over unreliable networks,” in 36th International Conference on Machine Learning, ICML 2019, Long Beach, CA, United States, 2019, vol. 2019–June, pp. 12481–12512.
View | Download (ext.) | arXiv
 
[29]
2019 | Conference Poster | IST-REx-ID: 6485
N. Koval, D.-A. Alistarh, and R. Elizarov, Lock-free channels for programming via communicating sequential processes. ACM Press, 2019, pp. 417–418.
View | DOI
 
[28]
2019 | Conference Paper | IST-REx-ID: 7201   OA
C. Renggli, S. Ashkboos, M. Aghagolzadeh, D.-A. Alistarh, and T. Hoefler, “SparCML: High-performance sparse communication for machine learning,” in International Conference for High Performance Computing, Networking, Storage and Analysis, SC, Denver, CO, Unites States, 2019.
View | DOI | Download (ext.) | arXiv
 
[27]
2019 | Conference Paper | IST-REx-ID: 6676   OA
D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Why extension-based proofs fail,” in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, Phoenix, AZ, United States, 2019, pp. 986–996.
View | DOI | Download (ext.) | arXiv
 
[26]
2018 | Conference Paper | IST-REx-ID: 5961
D.-A. Alistarh, “A brief tutorial on distributed and concurrent machine learning,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 487–488.
View | DOI
 
[25]
2018 | Conference Paper | IST-REx-ID: 5966   OA
D.-A. Alistarh, S. K. Haider, R. Kübler, and G. Nadiradze, “The transactional conflict problem,” in Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, Vienna, Austria, 2018, pp. 383–392.
View | DOI | Download (ext.) | arXiv
 
[24]
2018 | Conference Paper | IST-REx-ID: 6558   OA
D.-A. Alistarh, Z. Allen-Zhu, and J. Li, “Byzantine Stochastic Gradient Descent,” in Advances in Neural Information Processing Systems, Montreal, Canada, 2018, vol. Volume 2018, pp. 4613–4623.
View | Download (ext.) | arXiv
 
[23]
2018 | Conference Paper | IST-REx-ID: 6589   OA
D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” in Advances in Neural Information Processing Systems 31, Montreal, Canada, 2018, vol. Volume 2018, pp. 5973–5983.
View | Download (ext.) | arXiv
 
[22]
2018 | Journal Article | IST-REx-ID: 536   OA
D.-A. Alistarh, J. Aspnes, V. King, and J. Saia, “Communication-efficient randomized consensus,” Distributed Computing, vol. 31, no. 6, pp. 489–501, 2018.
View | Files available | DOI
 
[21]
2018 | Conference Paper | IST-REx-ID: 5962   OA
D.-A. Alistarh, C. De Sa, and N. 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, Egham, United Kingdom, 2018, pp. 169–178.
View | DOI | Download (ext.) | arXiv
 
[20]
2018 | Conference Paper | IST-REx-ID: 5963   OA
D.-A. Alistarh, T. A. Brown, J. Kopinsky, and G. Nadiradze, “Relaxed schedulers can efficiently parallelize iterative algorithms,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 377–386.
View | DOI | Download (ext.) | arXiv
 
[19]
2018 | Conference Paper | IST-REx-ID: 6031
A. Stojanov, T. M. Smith, D.-A. Alistarh, and M. Puschel, “Fast quantized arithmetic on x86: Trading compute for data movement,” in 2018 IEEE International Workshop on Signal Processing Systems, Cape Town, South Africa, 2018, vol. 2018–October.
View | DOI
 
[18]
2018 | Conference Paper | IST-REx-ID: 7116   OA
D. Grubic, L. Tam, D.-A. Alistarh, and C. 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, Vienna, Austria, 2018, pp. 145–156.
View | Files available | DOI
 
[17]
2018 | Conference Paper | IST-REx-ID: 7123   OA
D.-A. Alistarh, J. Aspnes, and R. Gelashvili, “Space-optimal majority in population protocols,” in Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, United States, 2018, pp. 2221–2239.
View | DOI | Download (ext.) | arXiv
 
[16]
2018 | Conference Paper | IST-REx-ID: 5964   OA
V. Aksenov, D.-A. Alistarh, and P. Kuznetsov, “Brief Announcement: Performance prediction for coarse-grained locking,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 411–413.
View | DOI | Download (ext.)
 
[15]
2018 | Journal Article | IST-REx-ID: 6001
D.-A. Alistarh, W. Leiserson, A. Matveev, and N. Shavit, “ThreadScan: Automatic and scalable memory reclamation,” ACM Transactions on Parallel Computing, vol. 4, no. 4, 2018.
View | Files available | DOI
 
[14]
2018 | Conference Paper | IST-REx-ID: 5965   OA
D.-A. Alistarh, T. A. Brown, J. Kopinsky, J. Z. Li, and G. Nadiradze, “Distributionally linearizable data structures,” in Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, Vienna, Austria, 2018, pp. 133–142.
View | DOI | Download (ext.) | arXiv
 
[13]
2017 | Conference Paper | IST-REx-ID: 787   OA
D.-A. Alistarh, J. Aspnes, D. Eisenstat, R. Rivest, and R. Gelashvili, “Time-space trade-offs in population protocols,” presented at the SODA: Symposium on Discrete Algorithms, 2017, pp. 2560–2579.
View | DOI | Download (ext.)
 
[12]
2017 | Conference Paper | IST-REx-ID: 788   OA
D.-A. Alistarh, B. Dudek, A. Kosowski, D. Soloveichik, and P. Uznański, “Robust detection in leak-prone population protocols,” presented at the DNA Computing and Molecular Programming, 2017, vol. 10467 LNCS, pp. 155–171.
View | DOI | Download (ext.)
 
[11]
2017 | Conference Paper | IST-REx-ID: 790
K. Kara, D.-A. Alistarh, G. Alonso, O. Mutlu, and C. Zhang, “FPGA-accelerated dense linear machine learning: A precision-convergence trade-off,” presented at the FCCM: Field-Programmable Custom Computing Machines, 2017, pp. 160–167.
View | DOI
 
[10]
2017 | Conference Paper | IST-REx-ID: 431   OA
D.-A. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnović, “QSGD: Communication-efficient SGD via gradient quantization and encoding,” presented at the NIPS: Neural Information Processing System, Long Beach, CA, United States, 2017, vol. 2017, pp. 1710–1721.
View | Download (ext.)
 
[9]
2017 | Conference Paper | IST-REx-ID: 789
D.-A. Alistarh, W. Leiserson, A. Matveev, and N. Shavit, “Forkscan: Conservative memory reclamation for modern operating systems,” presented at the EuroSys: European Conference on Computer Systems, 2017, pp. 483–498.
View | DOI
 
[8]
2017 | Conference Paper | IST-REx-ID: 791   OA
D.-A. Alistarh, J. Kopinsky, J. Li, and G. Nadiradze, “The power of choice in priority scheduling,” in Proceedings of the ACM Symposium on Principles of Distributed Computing, Washington, WA, USA, 2017, vol. Part F129314, pp. 283–292.
View | DOI | Download (ext.)
 
[7]
2017 | Conference Paper | IST-REx-ID: 432   OA
H. Zhang, J. Li, K. Kara, D.-A. Alistarh, J. Liu, and C. Zhang, “ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning,” in Proceedings of Machine Learning Research, Sydney, Australia, 2017, vol. 70, pp. 4035–4043.
View | Files available
 
[6]
2017 | Conference Paper | IST-REx-ID: 487
G. Baig, B. Radunovic, D.-A. Alistarh, M. Balkwill, T. Karagiannis, and L. Qiu, “Towards unlicensed cellular networks in TV white spaces,” in Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies, Incheon, South Korea, 2017, pp. 2–14.
View | DOI
 
[5]
2016 | Conference Paper | IST-REx-ID: 785
S. Haider, W. Hasenplaugh, and D.-A. Alistarh, “Lease/Release: Architectural support for scaling contended data structures,” presented at the PPoPP: Principles and Practice of Parallel Pogramming, 2016, vol. 12-16-March-2016.
View | DOI
 
[4]
2016 | Journal Article | IST-REx-ID: 786   OA
D.-A. Alistarh, K. Censor Hillel, and N. Shavit, “Are lock free concurrent algorithms practically wait free ,” Journal of the ACM, vol. 63, no. 4, 2016.
View | DOI | Download (ext.) | arXiv
 
[3]
2015 | Conference Paper | IST-REx-ID: 784
D.-A. Alistarh et al., “A high-radix, low-latency optical switch for data centers,” presented at the SIGCOMM: Special Interest Group on Data Communication, London, United Kindgdom, 2015, pp. 367–368.
View | DOI
 
[2]
2015 | Conference Paper | IST-REx-ID: 778   OA
D.-A. Alistarh, J. Kopinsky, P. Kuznetsov, S. Ravi, and N. Shavit, “Inherent limitations of hybrid transactional memory,” presented at the DISC: Distributed Computing, 2015, vol. 9363, pp. 185–199.
View | DOI | Download (ext.) | arXiv
 
[1]
2015 | Conference Paper | IST-REx-ID: 780   OA
D.-A. Alistarh and R. Gelashvili, “Polylogarithmic-time leader election in population protocols,” presented at the ICALP: International Colloquium on Automota, Languages and Programming, 2015, vol. 9135, pp. 479–491.
View | DOI | Download (ext.) | arXiv
 
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33 Publications

Mark all

[33]
2019 | Conference Paper | IST-REx-ID: 6673
D.-A. Alistarh, G. Nadiradze, and N. Koval, “Efficiency guarantees for parallel incremental algorithms under relaxed schedulers,” in 31st ACM Symposium on Parallelism in Algorithms and Architectures, Phoenix, AZ, United States, 2019, pp. 145–154.
View | DOI
 
[32]
2019 | Conference Paper | IST-REx-ID: 7122
S. Khirirat, M. Johansson, and D.-A. Alistarh, “Gradient compression for communication-limited convex optimization,” in 2018 IEEE Conference on Decision and Control, Miami Beach, FL, United States, 2019.
View | DOI
 
[31]
2019 | Conference Paper | IST-REx-ID: 7228
N. Koval, D.-A. Alistarh, and R. 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), Göttingen, Germany, 2019, vol. 11725, pp. 317–333.
View | DOI
 
[30]
2019 | Conference Paper | IST-REx-ID: 7437   OA
C. Yu et al., “Distributed learning over unreliable networks,” in 36th International Conference on Machine Learning, ICML 2019, Long Beach, CA, United States, 2019, vol. 2019–June, pp. 12481–12512.
View | Download (ext.) | arXiv
 
[29]
2019 | Conference Poster | IST-REx-ID: 6485
N. Koval, D.-A. Alistarh, and R. Elizarov, Lock-free channels for programming via communicating sequential processes. ACM Press, 2019, pp. 417–418.
View | DOI
 
[28]
2019 | Conference Paper | IST-REx-ID: 7201   OA
C. Renggli, S. Ashkboos, M. Aghagolzadeh, D.-A. Alistarh, and T. Hoefler, “SparCML: High-performance sparse communication for machine learning,” in International Conference for High Performance Computing, Networking, Storage and Analysis, SC, Denver, CO, Unites States, 2019.
View | DOI | Download (ext.) | arXiv
 
[27]
2019 | Conference Paper | IST-REx-ID: 6676   OA
D.-A. Alistarh, J. Aspnes, F. Ellen, R. Gelashvili, and L. Zhu, “Why extension-based proofs fail,” in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing, Phoenix, AZ, United States, 2019, pp. 986–996.
View | DOI | Download (ext.) | arXiv
 
[26]
2018 | Conference Paper | IST-REx-ID: 5961
D.-A. Alistarh, “A brief tutorial on distributed and concurrent machine learning,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 487–488.
View | DOI
 
[25]
2018 | Conference Paper | IST-REx-ID: 5966   OA
D.-A. Alistarh, S. K. Haider, R. Kübler, and G. Nadiradze, “The transactional conflict problem,” in Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, Vienna, Austria, 2018, pp. 383–392.
View | DOI | Download (ext.) | arXiv
 
[24]
2018 | Conference Paper | IST-REx-ID: 6558   OA
D.-A. Alistarh, Z. Allen-Zhu, and J. Li, “Byzantine Stochastic Gradient Descent,” in Advances in Neural Information Processing Systems, Montreal, Canada, 2018, vol. Volume 2018, pp. 4613–4623.
View | Download (ext.) | arXiv
 
[23]
2018 | Conference Paper | IST-REx-ID: 6589   OA
D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat, and C. Renggli, “The convergence of sparsified gradient methods,” in Advances in Neural Information Processing Systems 31, Montreal, Canada, 2018, vol. Volume 2018, pp. 5973–5983.
View | Download (ext.) | arXiv
 
[22]
2018 | Journal Article | IST-REx-ID: 536   OA
D.-A. Alistarh, J. Aspnes, V. King, and J. Saia, “Communication-efficient randomized consensus,” Distributed Computing, vol. 31, no. 6, pp. 489–501, 2018.
View | Files available | DOI
 
[21]
2018 | Conference Paper | IST-REx-ID: 5962   OA
D.-A. Alistarh, C. De Sa, and N. 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, Egham, United Kingdom, 2018, pp. 169–178.
View | DOI | Download (ext.) | arXiv
 
[20]
2018 | Conference Paper | IST-REx-ID: 5963   OA
D.-A. Alistarh, T. A. Brown, J. Kopinsky, and G. Nadiradze, “Relaxed schedulers can efficiently parallelize iterative algorithms,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 377–386.
View | DOI | Download (ext.) | arXiv
 
[19]
2018 | Conference Paper | IST-REx-ID: 6031
A. Stojanov, T. M. Smith, D.-A. Alistarh, and M. Puschel, “Fast quantized arithmetic on x86: Trading compute for data movement,” in 2018 IEEE International Workshop on Signal Processing Systems, Cape Town, South Africa, 2018, vol. 2018–October.
View | DOI
 
[18]
2018 | Conference Paper | IST-REx-ID: 7116   OA
D. Grubic, L. Tam, D.-A. Alistarh, and C. 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, Vienna, Austria, 2018, pp. 145–156.
View | Files available | DOI
 
[17]
2018 | Conference Paper | IST-REx-ID: 7123   OA
D.-A. Alistarh, J. Aspnes, and R. Gelashvili, “Space-optimal majority in population protocols,” in Proceedings of the 29th Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, United States, 2018, pp. 2221–2239.
View | DOI | Download (ext.) | arXiv
 
[16]
2018 | Conference Paper | IST-REx-ID: 5964   OA
V. Aksenov, D.-A. Alistarh, and P. Kuznetsov, “Brief Announcement: Performance prediction for coarse-grained locking,” in Proceedings of the 2018 ACM Symposium on Principles of Distributed Computing  - PODC ’18, Egham, United Kingdom, 2018, pp. 411–413.
View | DOI | Download (ext.)
 
[15]
2018 | Journal Article | IST-REx-ID: 6001
D.-A. Alistarh, W. Leiserson, A. Matveev, and N. Shavit, “ThreadScan: Automatic and scalable memory reclamation,” ACM Transactions on Parallel Computing, vol. 4, no. 4, 2018.
View | Files available | DOI
 
[14]
2018 | Conference Paper | IST-REx-ID: 5965   OA
D.-A. Alistarh, T. A. Brown, J. Kopinsky, J. Z. Li, and G. Nadiradze, “Distributionally linearizable data structures,” in Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures  - SPAA ’18, Vienna, Austria, 2018, pp. 133–142.
View | DOI | Download (ext.) | arXiv
 
[13]
2017 | Conference Paper | IST-REx-ID: 787   OA
D.-A. Alistarh, J. Aspnes, D. Eisenstat, R. Rivest, and R. Gelashvili, “Time-space trade-offs in population protocols,” presented at the SODA: Symposium on Discrete Algorithms, 2017, pp. 2560–2579.
View | DOI | Download (ext.)
 
[12]
2017 | Conference Paper | IST-REx-ID: 788   OA
D.-A. Alistarh, B. Dudek, A. Kosowski, D. Soloveichik, and P. Uznański, “Robust detection in leak-prone population protocols,” presented at the DNA Computing and Molecular Programming, 2017, vol. 10467 LNCS, pp. 155–171.
View | DOI | Download (ext.)
 
[11]
2017 | Conference Paper | IST-REx-ID: 790
K. Kara, D.-A. Alistarh, G. Alonso, O. Mutlu, and C. Zhang, “FPGA-accelerated dense linear machine learning: A precision-convergence trade-off,” presented at the FCCM: Field-Programmable Custom Computing Machines, 2017, pp. 160–167.
View | DOI
 
[10]
2017 | Conference Paper | IST-REx-ID: 431   OA
D.-A. Alistarh, D. Grubic, J. Li, R. Tomioka, and M. Vojnović, “QSGD: Communication-efficient SGD via gradient quantization and encoding,” presented at the NIPS: Neural Information Processing System, Long Beach, CA, United States, 2017, vol. 2017, pp. 1710–1721.
View | Download (ext.)
 
[9]
2017 | Conference Paper | IST-REx-ID: 789
D.-A. Alistarh, W. Leiserson, A. Matveev, and N. Shavit, “Forkscan: Conservative memory reclamation for modern operating systems,” presented at the EuroSys: European Conference on Computer Systems, 2017, pp. 483–498.
View | DOI
 
[8]
2017 | Conference Paper | IST-REx-ID: 791   OA
D.-A. Alistarh, J. Kopinsky, J. Li, and G. Nadiradze, “The power of choice in priority scheduling,” in Proceedings of the ACM Symposium on Principles of Distributed Computing, Washington, WA, USA, 2017, vol. Part F129314, pp. 283–292.
View | DOI | Download (ext.)
 
[7]
2017 | Conference Paper | IST-REx-ID: 432   OA
H. Zhang, J. Li, K. Kara, D.-A. Alistarh, J. Liu, and C. Zhang, “ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning,” in Proceedings of Machine Learning Research, Sydney, Australia, 2017, vol. 70, pp. 4035–4043.
View | Files available
 
[6]
2017 | Conference Paper | IST-REx-ID: 487
G. Baig, B. Radunovic, D.-A. Alistarh, M. Balkwill, T. Karagiannis, and L. Qiu, “Towards unlicensed cellular networks in TV white spaces,” in Proceedings of the 2017 13th International Conference on emerging Networking EXperiments and Technologies, Incheon, South Korea, 2017, pp. 2–14.
View | DOI
 
[5]
2016 | Conference Paper | IST-REx-ID: 785
S. Haider, W. Hasenplaugh, and D.-A. Alistarh, “Lease/Release: Architectural support for scaling contended data structures,” presented at the PPoPP: Principles and Practice of Parallel Pogramming, 2016, vol. 12-16-March-2016.
View | DOI
 
[4]
2016 | Journal Article | IST-REx-ID: 786   OA
D.-A. Alistarh, K. Censor Hillel, and N. Shavit, “Are lock free concurrent algorithms practically wait free ,” Journal of the ACM, vol. 63, no. 4, 2016.
View | DOI | Download (ext.) | arXiv
 
[3]
2015 | Conference Paper | IST-REx-ID: 784
D.-A. Alistarh et al., “A high-radix, low-latency optical switch for data centers,” presented at the SIGCOMM: Special Interest Group on Data Communication, London, United Kindgdom, 2015, pp. 367–368.
View | DOI
 
[2]
2015 | Conference Paper | IST-REx-ID: 778   OA
D.-A. Alistarh, J. Kopinsky, P. Kuznetsov, S. Ravi, and N. Shavit, “Inherent limitations of hybrid transactional memory,” presented at the DISC: Distributed Computing, 2015, vol. 9363, pp. 185–199.
View | DOI | Download (ext.) | arXiv
 
[1]
2015 | Conference Paper | IST-REx-ID: 780   OA
D.-A. Alistarh and R. Gelashvili, “Polylogarithmic-time leader election in population protocols,” presented at the ICALP: International Colloquium on Automota, Languages and Programming, 2015, vol. 9135, pp. 479–491.
View | DOI | Download (ext.) | arXiv
 
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