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The Landscape of Exascale Research: A Data-Driven Literature Analysis

Published: 20 March 2020 Publication History

Abstract

The next generation of supercomputers will break the exascale barrier. Soon we will have systems capable of at least one quintillion (billion billion) floating-point operations per second (1018 FLOPS). Tremendous amounts of work have been invested into identifying and overcoming the challenges of the exascale era. In this work, we present an overview of these efforts and provide insight into the important trends, developments, and exciting research opportunities in exascale computing. We use a three-stage approach in which we (1) discuss various exascale landmark studies, (2) use data-driven techniques to analyze the large collection of related literature, and (3) discuss eight research areas in depth based on influential articles. Overall, we observe that great advancements have been made in tackling the two primary exascale challenges: energy efficiency and fault tolerance. However, as we look forward, we still foresee two major concerns: the lack of suitable programming tools and the growing gap between processor performance and data bandwidth (i.e., memory, storage, networks). Although we will certainly reach exascale soon, without additional research, these issues could potentially limit the applicability of exascale computing.

References

[1]
2018. China Reveals Third Exascale Prototype | TOP500 Supercomputer Sites. https://www.top500.org/news/china-reveals-third-exascale-prototype/.
[2]
2018. Frontier: OLCF’s Exascale Future. https://www.olcf.ornl.gov/2018/02/13/frontier-olcfs-exascale-future/.
[3]
2018. Scopus - The Largest Database of Peer-Reviewed Literature. https://www.elsevier.com/solutions/scopus.
[4]
2018. TOP500 Supercomputer Sites. https://www.top500.org/. Accessed July 2018.
[5]
2019. BDEC: Big Data and Extreme-Scale Computing. https://www.exascale.org/bdec/.
[6]
2019. CAAR: Center for Accelerated Application Readiness. https://www.olcf.ornl.gov/caar/.
[7]
2019. EuroHPC: Europe’s Journey to Exascale HPC. http://eurohpc.eu/.
[8]
2019. IESP: International Exascale Software Project. https://www.exascale.org/iesp.
[9]
2019. U.S. Department of Energy and Intel to Deliver First Exascale Supercomputer, Argonne National Laboratory. https://www.anl.gov/article/us-department-of-energy-and-intel-to-deliver-first-exascale-supercomputer.
[10]
C. C. Aggarwal and C. X. Zhai. 2012. Mining Text Data. Springer Publishing Company, Inc.
[11]
J. H. Ahn, N. Binkert, A. Davis, M. McLaren, and R. S. Schreiber. 2009. HyperX: Topology, routing, and packaging of efficient large-scale networks. In Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis (SC’09). ACM, New York, 41:1--41:11.
[12]
Y. Ajima, S. Sumimoto, and T. Shimizu. 2009. Tofu: A 6D mesh/torus interconnect for exascale computers. Computer 42, 11 (Nov. 2009), 36--40.
[13]
M. Asch et al. 2018. Big data and extreme-scale computing: Pathways to convergence-toward a shaping strategy for a future software and data ecosystem for scientific inquiry. The International Journal of High Performance Computing Applications 32, 4 (July 2018), 435--479.
[14]
S. Ashby, P. Beckman, J. Chen, P. Colella, B. Collins, D. Crawford, J. Dongarra, D. Kothe, R. Lusk, P. Messina, et al. 2010. The Opportunities and Challenges of Exascale Computing. Technical Report. U.S. Department of Energy, Office of Science. Summary Report of the Advanced Scientific Computing Advisory Committee (ASCAC) Subcommittee.
[15]
J. A. Åström, A. Carter, J. Hetherington, K. Ioakimidis, E. Lindahl, G. Mozdzynski, R. W. Nash, P. Schlatter, A. Signell, and J. Westerholm. 2013. Preparing scientific application software for exascale computing. In Applied Parallel and Scientific Computing. Vol. 7782. Springer Berlin, Berlin, Germany, 27--42.
[16]
J. Bachan, D. Bonachea, P. H. Hargrove, S. Hofmeyr, M. Jacquelin, A. Kamil, B. Van Straalen, and S. B. Baden. 2017. The UPC++ PGAS library for exascale computing. In Proceedings of PAW 2017: 2nd Annual PGAS Applications Workshop - Held in Conjunction with SC 2017: The International Conference for High Performance Computing, Networking, Storage and Analysis, Vol. 2017-January. 1--4.
[17]
R. G. Beausoleil, M. McLaren, and N. P. Jouppi. 2013. Photonic architectures for high-performance data centers. IEEE Journal of Selected Topics in Quantum Electronics 19, 2 (March 2013), 3700109--3700109.
[18]
J. Bent, S. Faibish, J. Ahrens, G. Grider, J. Patchett, P. Tzelnic, and J. Woodring. 2012. Jitter-free co-processing on a prototype exascale storage stack. In Proceedings of the 2012 IEEE 28th Symposium on Mass Storage Systems and Technologies (MSST). 1--5.
[19]
M. W. Berry, M. Browne, A. N. Langville, V. P. Pauca, and R. J. Plemmons. 2007. Algorithms and applications for approximate nonnegative matrix factorization. Computational Statistics 8 Data Analysis 52, 1 (Sept. 2007), 155--173.
[20]
A. Bhatele, W. D. Gropp, N. Jain, and L. V. Kale. 2011. Avoiding hot-spots on two-level direct networks. In SC’11: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis. 1--11.
[21]
A. Bhatele, P. Jetley, H. Gahvari, L. Wesolowski, W. D. Gropp, and L. Kalé. 2011. Architectural constraints to attain 1 exaflop/s for three scientific application classes. In Proceedings of the 2011 IEEE International Parallel Distributed Processing Symposium. 80--91.
[22]
W. Bland. 2013. User level failure mitigation in MPI. In Euro-Par 2012: Parallel Processing Workshops. Vol. 7640. Springer Berlin, Berlin, Germany, 499--504.
[23]
W. Bland, P. Du, A. Bouteiller, T. Herault, G. Bosilca, and J. Dongarra. 2012. A checkpoint-on-failure protocol for algorithm-based recovery in standard MPI. In Euro-Par 2012 Parallel Processing (Lecture Notes in Computer Science). Springer Berlin, 477--488.
[24]
D. M. Blei, A. Y. Ng, and M. I. Jordan. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research 3, Jan (2003), 993--1022.
[25]
D. Bodas, J. Song, M. Rajappa, and A. Hoffman. 2014. Simple power-aware scheduler to limit power consumption by HPC system within a budget. In Proceedings of the 2nd International Workshop on Energy Efficient Supercomputing (E2SC’14). IEEE Press, Piscataway, NJ, 21--30.
[26]
A. Borghesi, A. Bartolini, M. Lombardi, M. Milano, and L. Benini. 2016. Predictive modeling for job power consumption in HPC systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) vol. 9697 (2016), 181--199.
[27]
G. Bosilca, T. Herault, A. Rezmerita, and J. Dongarra. 2011. On scalability for MPI runtime systems. In Proceedings of the 2011 IEEE International Conference on Cluster Computing. 187--195.
[28]
K. W. Boyack, D. Newman, R. J. Duhon, R. Klavans, M. Patek, J. R. Biberstine, B. Schijvenaars, A. Skupin, N. Ma, and K. Börner. 2011. Clustering more than two million biomedical publications: Comparing the accuracies of nine text-based similarity approaches. PLoS one 6, 3 (2011).
[29]
P. C. Broekema, R. V. van Nieuwpoort, and H. E. Bal. 2012. ExaScale high performance computing in the square kilometer array. In Proceedings of the 2012 Workshop on High-Performance Computing for Astronomy Date - Astro-HPC’12. ACM, Delft, The Netherlands, 9.
[30]
A. Cabrera, F. Almeida, J. Arteaga, and V. Blanco. 2015. Measuring energy consumption using EML (energy measurement library). Comput. Sci. Res. Dev. 30, 2 (May 2015), 135--143.
[31]
F. Cappello. 2009. Fault tolerance in petascale/exascale systems: Current knowledge, challenges and research opportunities. The International Journal of High Performance Computing Applications 23, 3 (Aug. 2009), 212--226.
[32]
F. Cappello, A. Geist, B. Gropp, L. Kale, B. Kramer, and M. Snir. 2009. Toward exascale resilience. The International Journal of High Performance Computing Applications 23, 4 (Nov. 2009), 374--388.
[33]
F. Cappello, A. Geist, W. Gropp, S. Kale, B. Kramer, and M. Snir. 2014. Toward exascale resilience: 2014 update. Supercomputing Frontiers and Innovations 1, 1 (June 2014), 5--28--28.
[34]
H. Carter Edwards, C. R. Trott, and D. Sunderland. 2014. Kokkos: Enabling manycore performance portability through polymorphic memory access patterns. J. Parallel and Distrib. Comput. 74, 12 (Dec. 2014), 3202--3216.
[35]
C. Chan, D. Unat, M. Lijewski, W. Zhang, J. Bell, and J. Shalf. 2013. Software design space exploration for exascale combustion co-design. In Supercomputing (Lecture Notes in Computer Science). Springer Berlin, 196--212.
[36]
R. R. Chandrasekar, A. Venkatesh, K. Hamidouche, and D. K. Panda. 2015. Power-check: An energy-efficient checkpointing framework for HPC clusters. In Proceedings of the 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. 261--270.
[37]
A. Chien, P. Balaji, P. Beckman, N. Dun, A. Fang, H. Fujita, K. Iskra, Z. Rubenstein, Z. Zheng, R. Schreiber, J. Hammond, J. Dinan, I. Laguna, D. Richards, A. Dubey, B. van Straalen, M. Hoemmen, M. Heroux, K. Teranishi, and A. Siegel. 2015. Versioned distributed arrays for resilience in scientific applications: Global view resilience. Procedia Computer Science 51 (Jan. 2015), 29--38.
[38]
J. Choo, C. Lee, C. K. Reddy, and H. Park. 2013. UTOPIAN: User-driven topic modeling based on interactive nonnegative matrix factorization. IEEE Transactions on Visualization and Computer Graphics 19, 12 (Dec. 2013), 1992--2001.
[39]
J. Chung, I. Lee, M. Sullivan, J. H. Ryoo, D. W. Kim, D. H. Yoon, L. Kaplan, and M. Erez. 2013. Containment domains: A scalable, efficient and flexible resilience scheme for exascale systems. Scientific Programming. https://www.hindawi.com/journals/sp/2013/473915/abs/.
[40]
G. Congiu, S. Narasimhamurthy, T. Süß, and A. Brinkmann. 2016. Improving collective I/O performance using non-volatile memory devices. In Proceedings of the IEEE International Conference on Cluster Computing, (ICCC). 120--129.
[41]
G. Da Costa, T. Fahringer, J. A. R. Gallego, I. Grasso, A. Hristov, H. D. Karatza, A. Lastovetsky, F. Marozzo, D. Petcu, G. L. Stavrinides, D. Talia, P. Trunfio, and H. Astsatryan. 2015. Exascale machines require new programming paradigms and runtimes. Supercomputing Frontiers and Innovations 2, 2 (Aug. 2015), 6--27.
[42]
P. Czarnul, J. Proficz, and A. Krzywaniak. 2019. Energy-aware high-performance computing: Survey of state-of-the-art tools, techniques, and environments. Scientific Programming (2019).
[43]
K. Czechowski, C. Battaglino, C. McClanahan, K. Iyer, P. Yeung, and R. Vuduc. 2012. On the communication complexity of 3D FFTs and its implications for exascale. In Proceedings of the 26th ACM International Conference on Supercomputing (ICS’12). ACM Press, San Servolo Island, Venice, Italy, 205.
[44]
J. Daily, A. Vishnu, B. Palmer, H. van Dam, et al. 2014. On the suitability of MPI as a PGAS runtime. In 2014 21st International Conference on High Performance Computing (HiPC). 1--10.
[45]
A. Danalis, G. Bosilca, A. Bouteiller, T. Herault, and J. Dongarra. 2014. PTG: An abstraction for unhindered parallelism. In Proceedings of WOLFHPC 2014: 4th International Workshop on Domain-Specific Languages and High-Level Frameworks for High Performance Computing - Held in Conjunction with SC 2014: The International Conference for High Performance Computing, Networking, Storage and Analysis. 21--30.
[46]
D. Dauwe, R. Jhaveri, S. Pasricha, A. A. Maciejewski, and H. J. Siegel. 2018. Optimizing checkpoint intervals for reduced energy use in exascale systems. In Proceedings of the 2017 8th International Green and Sustainable Computing Conference, (IGSC'17). 1--8.
[47]
W. Deconinck, P. Bauer, M. Diamantakis, M. Hamrud, C. Kühnlein, P. Maciel, G. Mengaldo, T. Quintino, B. Raoult, P. K. Smolarkiewicz, and N. P. Wedi. 2017. Atlas: A library for numerical weather prediction and climate modelling. Computer Physics Communications 220 (Nov. 2017), 188--204.
[48]
S. Derradji, T. Palfer-Sollier, J. Panziera, A. Poudes, and F. W. Atos. 2015. The BXI interconnect architecture. In Proceedings of the 2015 IEEE 23rd Annual Symposium on High-Performance Interconnects. 18--25.
[49]
S. Di, M. S. Bouguerra, L. Bautista-Gomez, and F. Cappello. 2014. Optimization of multi-level checkpoint model for large scale HPC applications. In Proceedings of the 2014 IEEE 28th International Parallel and Distributed Processing Symposium. 1181--1190.
[50]
S. Di and F. Cappello. 2016. Adaptive impact-driven detection of silent data corruption for HPC applications. IEEE Transactions on Parallel and Distributed Systems 27, 10 (2016), 2809--2823.
[51]
J. Diaz, C. Muñoz-Caro, and A. Niño. 2012. A survey of parallel programming models and tools in the multi and many-core era. IEEE Transactions on Parallel and Distributed Systems 23, 8 (Aug. 2012), 1369--1386.
[52]
X. Dong, N. Muralimanohar, N. Jouppi, R. Kaufmann, and Y. Xie. 2009. Leveraging 3D PCRAM technologies to reduce checkpoint overhead for future exascale systems. In Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis. 1--12.
[53]
X. Dong, Y. Xie, N. Muralimanohar, and N. P. Jouppi. 2011. Hybrid checkpointing using emerging nonvolatile memories for future exascale systems. ACM Transactions on Architecture and Code Optimization 8, 2 (July 2011), 1--29.
[54]
J. Dongarra, P. Beckman, T. Moore, P. Aerts, G. Aloisio, J. Andre, D. Barkai, J. Berthou, T. Boku, B. Braunschweig, et al. 2011. The international exascale software project roadmap. International Journal of High Performance Computing Applications 25, 1 (2011), 3--60.
[55]
J. Dongarra, M. Faverge, T. Hérault, M. Jacquelin, J. Langou, and Y. Robert. 2013. Hierarchical QR factorization algorithms for multi-core clusters. Parallel Comput. 39, 4 (April 2013), 212--232.
[56]
J. J. Dongarra. 2014. Performance of Various Computers Using Standard Linear Equations Software. Technical CS-89-85. University of Manchester. 110 pages.
[57]
M. Dorier, G. Antoniu, F. Cappello, M. Snir, R. Sisneros, O. Yildiz, S. Ibrahim, T. Peterka, and L. Orf. 2016. Damaris: Addressing performance variability in data management for post-petascale simulations. ACM Transactions on Parallel Computing 3, 3 (2016).
[58]
M. Dorier, M. Mubarak, R. Ross, J. K. Li, C. D. Carothers, and K. Ma. 2016. Evaluation of topology-aware broadcast algorithms for dragonfly networks. In Proceedings of the 2016 IEEE International Conference on Cluster Computing (CLUSTER). 40--49.
[59]
M. Dorier, R. Sisneros, T. Peterka, G. Antoniu, and D. Semeraro. 2013. Damaris/viz: A nonintrusive, adaptable and user-friendly in situ visualization framework. In Proceedings of the 2013 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV). 67--75.
[60]
S. S. Dosanjh, R. F. Barrett, D. W. Doerfler, S. D. Hammond, K. S. Hemmert, M. A. Heroux, P. T. Lin, K. T. Pedretti, A. F. Rodrigues, T. G. Trucano, and J. P. Luitjens. 2014. Exascale design space exploration and co-design. Future Generation Computer Systems 30 (Jan. 2014), 46--58.
[61]
S. T. Dumais. 2004. Latent semantic analysis. Annual Review of Information Science and Technology 38, 1 (2004), 188--230.
[62]
N. Dun, H. Fujita, J. R. Tramm, A. A. Chien, and A. R. Siegel. 2015. Data decomposition in Monte Carlo neutron transport simulations using global view arrays. International Journal of High Performance Computing Applications 29, 3 (2015), 348--365.
[63]
N. Eicker, T. Lippert, T. Moschny, and E. Suarez. 2013. The DEEP project - Pursuing cluster-computing in the many-core era. In Proceedings of the 2013 42nd International Conference on Parallel Processing. 885--892.
[64]
N. El-Sayed and B. Schroeder. 2013. Reading between the lines of failure logs: Understanding how HPC systems fail. In Proceedings of the 2013 43rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). 1--12.
[65]
C. Engelmann. 2014. Scaling to a million cores and beyond: Using light-weight simulation to understand the challenges ahead on the road to exascale. Future Generation Computer Systems 30 (Jan. 2014), 59--65.
[66]
“European Exascale Software Initiative”. 2015. Final Report on EESI2 Exascale Vision, Roadmap, and Recommendations. http://www.eesi-project.eu/ressources/documentation/.
[67]
N. Fabian, K. Moreland, D. Thompson, A. C. Bauer, et al. 2011. The ParaView coprocessing library: A scalable, general purpose in situ visualization library. In Proceedings of the 2011 IEEE Symposium on Large Data Analysis and Visualization. 89--96.
[68]
K. Ferreira, J. Stearley, J. H. Laros, R. Oldfield, et al. 2011. Evaluating the viability of process replication reliability for exascale systems. In Proceedings of the 2011 International Conference for High Performance Computing, Networking, Storage and Analysis (SC’11). ACM, Seattle, Washington, 1.
[69]
R. Ferreira da Silva, S. Callaghan, T. M. A. Do, G. Papadimitriou, and E. Deelman. 2019. Measuring the impact of burst buffers on data-intensive scientific workflows. Future Generation Computer Systems 101 (2019), 208--220.
[70]
D. Fiala. 2011. Detection and correction of silent data corruption for large-scale high-performance computing. In Proceedings of the 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum. 2069--2072.
[71]
M. Flajslik, E. Borch, and M. A. Parker. 2018. Megafly: A topology for exascale systems. In High Performance Computing (Lecture Notes in Computer Science). Springer International Publishing, 289--310.
[72]
K. Fürlinger. 2015. Exploiting hierarchical exascale hardware using a PGAS approach. In Proceedings of the 3rd International Conference on Exascale Applications and Software (EASC’15). University of Edinburgh, Edinburgh, Scotland, UK, 48--52.
[73]
H. Gahvari and W. Gropp. 2010. An introductory exascale feasibility study for FFTs and multigrid. In 2010 IEEE International Symposium on Parallel Distributed Processing (IPDPS). 1--9.
[74]
M. Gamell, D. S. Katz, H. Kolla, J. Chen, S. Klasky, and M. Parashar. 2014. Exploring automatic, online failure recovery for scientific applications at extreme scales. In SC’14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 895--906.
[75]
A. Geist and R. Lucas. 2009. Major computer science challenges at exascale. The International Journal of High Performance Computing Applications 23, 4 (2009), 427--436.
[76]
B. Gerofi, Y. Ishikawa, R. Riesen, R. W. Wisniewski, Y. Park, and B. Rosenburg. 2016. A multi-kernel survey for high-performance computing. In Proceedings of the 6th International Workshop on Runtime and Operating Systems for Supercomputers (ROSS’16). ACM, New York, 5:1--5:8.
[77]
B. Gerofi, M. Takagi, Y. Ishikawa, R. Riesen, E. Powers, and R. W. Wisniewski. 2015. Exploring the design space of combining Linux with lightweight kernels for extreme scale computing. In Proceedings of the 5th International Workshop on Runtime and Operating Systems for Supercomputers - ROSS’15. ACM, Portland, OR, 1--8.
[78]
A. Gholami, D. Malhotra, H. Sundar, and G. Biros. 2016. FFT, FMM, or multigrid? A comparative study of state-of-the-art poisson solvers for uniform and nonuniform grids in the unit cube. SIAM Journal on Scientific Computing 38, 3 (Jan. 2016), C280--C306.
[79]
N. Gholkar, F. Mueller, and B. Rountree. 2016. Power tuning HPC jobs on power-constrained systems. In Proceedings of the 2016 International Conference on Parallel Architectures and Compilation (PACT’16). ACM, New York, 179--191.
[80]
P. Ghysels, T. J. Ashby, K. Meerbergen, and W. Vanroose. 2013. Hiding global communication latency in the GMRES algorithm on massively parallel machines. SIAM Journal on Scientific Computing (Jan. 2013).
[81]
M. Giampapa, T. Gooding, T. Inglett, and R. W. Wisniewski. 2010. Experiences with a lightweight supercomputer kernel: Lessons learned from Blue Gene’s CNK. In SC’10: Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis. 1--10.
[82]
B. Giridhar, M. Cieslak, D. Duggal, R. Dreslinski, H. M. Chen, R. Patti, B. Hold, C. Chakrabarti, T. Mudge, and D. Blaauw. 2013. Exploring DRAM organizations for energy-efficient and resilient exascale memories. In SC’13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. 1--12.
[83]
L. A. B. Gomez and F. Cappello. 2015. Detecting and correcting data corruption in stencil applications through multivariate interpolation. In Proceedings of the 2015 IEEE International Conference on Cluster Computing. 595--602.
[84]
W. Gropp and M. Snir. 2013. Programming for exascale computers. Computing in Science Engineering 15, 6 (Nov. 2013), 27--35.
[85]
S. Gupta, T. Patel, C. Engelmann, and D. Tiwari. 2017. Failures in large scale systems: Long-term measurement, analysis, and implications. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, (SC'17).
[86]
A. Haidar, H. Jagode, P. Vaccaro, A. YarKhan, S. Tomov, and J. Dongarra. 2019. Investigating power capping toward energy-efficient scientific applications. Concurrency and Computation: Practice and Experience (March 2019).
[87]
F. Hariri, T. M. Tran, A. Jocksch, E. Lanti, J. Progsch, P. Messmer, S. Brunner, C. Gheller, and L. Villard. 2016. A portable platform for accelerated PIC codes and its application to GPUs using OpenACC. Computer Physics Communications 207 (2016), 69--82.
[88]
K. Hasanov, J. Quintin, and A. Lastovetsky. 2015. Hierarchical approach to optimization of parallel matrix multiplication on large-scale platforms. The Journal of Supercomputing 71, 11 (Nov. 2015), 3991--4014.
[89]
A. Hayashi, S. R. Paul, M. Grossman, J. Shirako, and V. Sarkar. 2017. Chapel-on-X: Exploring tasking runtimes for PGAS languages. In Proceedings of the 3rd International Workshop on Extreme Scale Programming Models and Middleware (ESPM2’17). ACM, New York, 5:1--5:8.
[90]
A. Heirich, E. Slaughter, M. Papadakis, W. Lee, T. Biedert, and A. Aiken. 2017. In situ visualization with task-based parallelism. In Proceedings of the In Situ Infrastructures on Enabling Extreme-Scale Analysis and Visualization (ISAV’17). ACM, Denver, CO, 17--21.
[91]
Stijn Heldens, Alessio Sclocco, and Henk Dreuning. 2019. NLeSC/automated-literature-analysis.
[92]
T. Herault and Y. Robert. 2015. Fault-Tolerance Techniques for High-Performance Computing. Springer, Cham Heidelberg New York Dordrecht London.
[93]
W. Hu, G. Liu, Q. Li, Y. Jiang, and G. Cai. 2016. Storage wall for exascale supercomputing. Frontiers of Information Technology 8 Electronic Engineering 17, 11 (Nov. 2016), 1154--1175.
[94]
M. Huber, B. Gmeiner, U. Rüde, and B. Wohlmuth. 2016. Resilience for massively parallel multigrid solvers. SIAM Journal on Scientific Computing 38, 5 (Jan. 2016), S217--S239.
[95]
S. Hukerikar and R. F. Lucas. 2016. Rolex: Resilience-oriented language extensions for extreme-scale systems. The Journal of Supercomputing 72, 12 (Dec. 2016), 4662--4695.
[96]
D. Ibtesham, D. Arnold, P. G. Bridges, K. B. Ferreira, and R. Brightwell. 2012. On the viability of compression for reducing the overheads of checkpoint/restart-based fault tolerance. In Proceedings of the 2012 41st International Conference on Parallel Processing. 148--157.
[97]
C. Iwainsky, S. Shudler, A. Calotoiu, A. Strube, M. Knobloch, C. Bischof, and F. Wolf. 2015. How many threads will be too many? On the scalability of OpenMP implementations. In Euro-Par 2015: Parallel Processing. vol. 9233. Springer Berlin, Berlin, Germany, 451--463.
[98]
C. Jin, B. R. de Supinski, D. Abramson, H. Poxon, L. DeRose, M. N. Dinh, M. Endrei, and E. R. Jessup. 2017. A survey on software methods to improve the energy efficiency of parallel computing. The International Journal of High Performance Computing Applications 31, 6 (Nov. 2017), 517--549.
[99]
H. Jin, D. Jespersen, P. Mehrotra, R. Biswas, L. Huang, and B. Chapman. 2011. High performance computing using MPI and OpenMP on multi-core parallel systems. Parallel Comput. 37, 9 (Sept. 2011), 562--575.
[100]
H. Kaiser, T. Heller, B. Adelstein-Lelbach, A. Serio, and D. Fey. 2014. HPX: A task based programming model in a global address space. In Proceedings of the 8th International Conference on Partitioned Global Address Space Programming Models (PGAS’14). ACM, Eugene, OR, 1--11.
[101]
D. A. Kane, P. Rogé, and S. S. Snapp. 2016. A systematic review of perennial staple crops literature using topic modeling and bibliometric analysis. PLoS one 11, 5 (May 2016).
[102]
S. Kannan, A. Gavrilovska, K. Schwan, D. Milojicic, and V. Talwar. 2011. Using active NVRAM for I/O staging. In Proceedings of the 2nd International Workshop on Petascal Data Analytics: Challenges and Opportunities (PDAC’11). ACM, Seattle, Washington, 15.
[103]
P. Kogge, K. Bergman, S. Borkar, D. Campbell, W. Carlson, W. Dally, M. Denneau, P. Franzon, W. Harrod, K. Hill, J. Hiller, et al. 2008. Exascale Computing Study: Technology Challenges in Achieving Exascale Systems. Technical Report. Defense Advanced Research Projects Agency Information Processing Techniques Office (DARPA IPTO).
[104]
P. Kogge and J. Shalf. 2013. Exascale computing trends: Adjusting to the “new normal” for computer architecture. Computing in Science Engineering 15, 6 (Nov. 2013), 16--26.
[105]
J. M. Kunkel, M. Kuhn, and T. Ludwig. 2014. Exascale storage systems -- An analytical study of expenses. Supercomputing Frontiers and Innovations 1, 1 (June 2014), 116--134--134.
[106]
Oak Ridge National Laboratory. 2019. Summit. https://www.olcf.ornl.gov/olcf-resources/compute-systems/summit/.
[107]
B. N. Lawrence, M. Rezny, R. Budich, P. Bauer, J. Behrens, M. Carter, W. Deconinck, R. Ford, C. Maynard, S. Mullerworth, C. Osuna, A. Porter, K. Serradell, S. Valcke, N. Wedi, and S. Wilson. 2018. Crossing the chasm: How to develop weather and climate models for next generation computers? Geoscientific Model Development 11, 5 (May 2018), 1799--1821.
[108]
S. Lee and J. S. Vetter. 2012. Early evaluation of directive-based GPU programming models for productive exascale computing. In SC’12: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. 1--11.
[109]
D. Li, J. S. Vetter, G. Marin, C. McCurdy, C. Cira, Z. Liu, and W. Yu. 2012. Identifying opportunities for byte-addressable non-volatile memory in extreme-scale scientific applications. In Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium. 945--956.
[110]
X. Liao, C. Yung, T. Tang, H. Yi, F. Wang, Q. Wu, and J. Xue. 2014. OpenMC: Towards simplifying programming for TianHe supercomputers. J. Comput. Sci. Technol. 29, 3 (May 2014), 532--546.
[111]
J. Liu and G. Agrawal. 2016. Soft error detection for iterative applications using offline training. In Proceedings of the 2016 IEEE 23rd International Conference on High Performance Computing (HiPC). 2--11.
[112]
Q. Liu, J. Logan, Y. Tian, H. Abbasi, N. Podhorszki, J. Y. Choi, S. Klasky, R. Tchoua, J. Lofstead, R. Oldfield, M. Parashar, N. Samatova, K. Schwan, A. Shoshani, M. Wolf, K. Wu, and W. Yu. 2014. Hello ADIOS: The challenges and lessons of developing leadership class I/O frameworks. Concurrency and Computation: Practice and Experience 26, 7 (May 2014), 1453--1473.
[113]
J. Lofstead, I. Jimenez, C. Maltzahn, Q. Koziol, J. Bent, and E. Barton. 2016. DAOS and friends: A proposal for an exascale storage system. In SC’16: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 585--596.
[114]
Los Alamos Lab. 2019. High-Performance Computing: Roadrunner. http://www.lanl.gov/roadrunner/.
[115]
R. Lucas et al. 2014. Top Ten Exascale Research Challenges. Technical Report. U.S. Department of Energy, Office of Science. DEO ASCAC Subcommittee Report.
[116]
J. Lüttgau, M. Kuhn, K. Duwe, Y. Alforov, E. Betke, J. Kunkel, and T. Ludwig. 2018. Survey of storage systems for high-performance computing. Supercomputing Frontiers and Innovations 5, 1 (April 2018), 31--58.
[117]
K. Ma. 2009. In situ visualization at extreme scale: Challenges and opportunities. IEEE Computer Graphics and Applications 29, 6 (2009), 14--19.
[118]
T. Maeno, K. De, T. Wenaus, P. Nilsson, G. A. Stewart, R. Walker, A. Stradling, J. Caballero, M. Potekhin, D. Smith, and T. A. Collaboration. 2011. Overview of ATLAS PanDAWorkload Management. J. Phys. Conf. Ser. 331, 7 (2011), 072024. https://doi.org/10.1088/1742-6596/331/7/072024
[119]
T. Maeno, K. De, T. Wenaus, P. Nilsson, G. A. Stewart, R. Walker, A. Stradling, J. Caballero, M. Potekhin, D. Smith, and T. A. Collaboration. 2011. Overview of ATLAS PanDA workload management. J. Phys.: Conf. Ser. 331, 7 (2011), 072024.
[120]
J. Mair, Z. Huang, D. Eyers, and Y. Chen. 2015. Quantifying the energy efficiency challenges of achieving exascale computing. In Proceedings of the 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. 943--950.
[121]
S. Markidis, I. B. Peng, J. Larsson Träff, A. Rougier, V. Bartsch, R. Machado, M. Rahn, A. Hart, D. Holmes, M. Bull, and E. Laure. 2016. The EPiGRAM project: Preparing parallel programming models for exascale. In High Performance Computing. Vol. 9945. Springer International Publishing, Cham, 56--68.
[122]
C. D. Martino, W. Kramer, Z. Kalbarczyk, and R. Iyer. 2015. Measuring and understanding extreme-scale application resilience: A field study of 5,000,000 HPC application runs. In Proceedings of the International Conference on Dependable Systems and Networks, Vol. 2015-September. 25--36.
[123]
G. Mathew, A. Agrawal, and T. Menzies. 2017. Trends in topics at SE conferences (1993-2013). In Proceedings of the 2017 IEEE/ACM 39th International Conference on Software Engineering Companion (ICSE-C). 397--398.
[124]
T. G. Mattson, R. Cledat, V. Cavé, V. Sarkar, Z. Budimlić, S. Chatterjee, J. Fryman, I. Ganev, R. Knauerhase, M. Lee, B. Meister, B. Nickerson, N. Pepperling, B. Seshasayee, S. Tasirlar, J. Teller, and N. Vrvilo. 2016. The open community runtime: A runtime system for extreme scale computing. In Proceedings of the 2016 IEEE High Performance Extreme Computing Conference (HPEC). 1--7.
[125]
E. Meneses, X. Ni, G. Zheng, C. L. Mendes, and L. V. Kalé. 2015. Using migratable objects to enhance fault tolerance schemes in supercomputers. IEEE Transactions on Parallel and Distributed Systems 26, 7 (July 2015), 2061--2074.
[126]
P. Messina. 2017. The exascale computing project. Computing in Science 8 Engineering 19, 3 (May 2017), 63--67.
[127]
M. R. Meswani, G. H. Loh, S. Blagodurov, D. Roberts, J. Slice, and M. Ignatowski. 2014. Toward efficient programmer-managed two-level memory hierarchies in exascale computers. In Proceedings of the 2014 Hardware-Software Co-Design for High Performance Computing. 9--16.
[128]
G. Mitra, E. Stotzer, A. Jayaraj, and A. P. Rendell. 2014. Implementation and optimization of the OpenMP accelerator model for the TI keystone II architecture. In Using and Improving OpenMP for Devices, Tasks, and More (Lecture Notes in Computer Science). Springer International Publishing, 202--214.
[129]
S. Mittal and J. S. Vetter. 2015. A survey of CPU-GPU heterogeneous computing techniques. ACM Comput. Surv. 47, 4 (July 2015), 69:1--69:35.
[130]
K. Moreland, U. Ayachit, B. Geveci, and K. Ma. 2011. Dax toolkit: A proposed framework for data analysis and visualization at extreme scale. In Proceedings of the 2011 IEEE Symposium on Large Data Analysis and Visualization. 97--104.
[131]
M. Mubarak, C. D. Carothers, R. B. Ross, and P. Carns. 2017. Enabling parallel simulation of large-scale HPC network systems. IEEE Transactions on Parallel and Distributed Systems 28, 1 (2017), 87--100.
[132]
R. Nair et al. 2015. Active memory cube: A processing-in-memory architecture for exascale systems. IBM Journal of Research and Development 59, 2/3 (March 2015), 17:1--17:14.
[133]
S. Narasimhamurthy, N. Danilov, S. Wu, G. Umanesan, S. W. D. Chien, S. Rivas-Gomez, I. B. Peng, E. Laure, S. De Witt, D. Pleiter, and S. Markidis. 2018. The SAGE project: A storage centric approach for exascale computing. In Proceedings of the 2018 ACM International Conference on Computing Frontiers, (CF 2018). 287--292.
[134]
T. Naughton, G. Smith, C. Engelmann, G. Vallée, F. Aderholdt, and S. L. Scott. 2014. What is the right balance for performance and isolation with virtualization in HPC? In Euro-Par 2014: Parallel Processing Workshops (Lecture Notes in Computer Science). Springer International Publishing, 570--581.
[135]
V. P. Nikolskiy, V. V. Stegailov, and V. S. Vecher. 2016. Efficiency of the Tegra K1 and X1 systems-on-chip for classical molecular dynamics. In Proceedings of the 2016 International Conference on High Performance Computing Simulation (HPCS). 682--689.
[136]
Oak Ridge National Laboratory. 2019. Titan. https://www.olcf.ornl.gov/olcf-resources/compute-systems/titan/.
[137]
K. O’Brien, L. D. Tucci, G. Durelli, and M. Blott. 2017. Towards exascale computing with heterogeneous architectures. In Proceedings of the Design, Automation Test in Europe Conference Exhibition (DATE), 2017. 398--403.
[138]
J. Ouyang, B. Kocoloski, J. R. Lange, and K. Pedretti. 2015. Achieving performance isolation with lightweight co-kernels. In Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing (HPDC’15). ACM, Portland, Oregon, 149--160.
[139]
S. Páll, M. J. Abraham, C. Kutzner, B. Hess, and E. Lindahl. 2015. Tackling exascale software challenges in molecular dynamics simulations with GROMACS. In Solving Software Challenges for Exascale. Vol. 8759. Springer International Publishing, Cham, 3--27.
[140]
J. A. Pascual, J. Lant, A. Attwood, C. Concatto, J. Navaridas, M. Lujan, and J. Goodacre. 2017. Designing an exascale interconnect using multi-objective optimization. In Proceedings of the 2017 IEEE Congress on Evolutionary Computation, (CEC 2017). 2209--2216.
[141]
T. Patki, D. K. Lowenthal, B. Rountree, M. Schulz, and B. R. de Supinski. 2013. Exploring hardware overprovisioning in power-constrained, high performance computing. In Proceedings of the 27th International ACM Conference on International Conference on Supercomputing (ICS’13). ACM, Eugene, Oregon, 173.
[142]
V. Pauca, F. Shahnaz, M. Berry, and R. Plemmons. 2004. Text mining using non-negative matrix factorizations. In Proceedings of the 2004 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 452--456.
[143]
S. Perarnau, R. Thakur, K. Iskra, K. Raffenetti, F. Cappello, R. Gupta, P. Beckman, M. Snir, H. Hoffmann, M. Schulz, and B. Rountree. 2015. Distributed monitoring and management of exascale systems in the Argo project. In Distributed Applications and Interoperable Systems (Lecture Notes in Computer Science). Springer International Publishing, 173--178.
[144]
S. Perarnau, J. A. Zounmevo, M. Dreher, B. C. V. Essen, R. Gioiosa, K. Iskra, M. B. Gokhale, K. Yoshii, and P. Beckman. 2017. Argo NodeOS: Toward unified resource management for exascale. In Proceedings of the 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS). 153--162.
[145]
S. Pickartz, S. Lankes, A. Monti, C. Clauss, and J. Breitbart. 2016. Application migration in HPC — A driver of the exascale era? In Proceedings of the 2016 International Conference on High Performance Computing Simulation (HPCS). 318--325.
[146]
M. F. Porter. 1980. An algorithm for suffix stripping. Program 14, 3 (1980), 130--137.
[147]
A. K. Porterfield, S. L. Olivier, S. Bhalachandra, and J. F. Prins. 2013. Power measurement and concurrency throttling for energy reduction in OpenMP programs. In 2013 IEEE International Symposium on Parallel Distributed Processing, Workshops and Phd Forum. 884--891.
[148]
B. Prisacari, G. Rodriguez, P. Heidelberger, D. Chen, C. Minkenberg, and T. Hoefler. 2014. Efficient task placement and routing of nearest neighbor exchanges in dragonfly networks. In Proceedings of the 23rd International Symposium on High-Performance Parallel and Distributed Computing - HPDC’14. ACM Press, Vancouver, BC, Canada, 129--140.
[149]
N. Rajovic et al. 2016. The Mont-Blanc prototype: An alternative approach for HPC systems. In SC’16: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 444--455.
[150]
C. Rasmussen, M. Sottile, S. Rasmussen, D. Nagle, and W. Dumas. 2016. CAFe: Coarray fortran extensions for heterogeneous computing. In 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 357--365.
[151]
D. A. Reed and J. Dongarra. 2015. Exascale computing and big data. Commun. ACM 58, 7 (2015), 56--68.
[152]
P. M. Reed and D. Hadka. 2014. Evolving many-objective water management to exploit exascale computing. Water Resources Research 50, 10 (2014), 8367--8373.
[153]
L. Rokach and O. Maimon. 2005. Clustering methods. In Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA, 321--352.
[154]
S. Rumley, M. Bahadori, R. Polster, S. D. Hammond, D. M. Calhoun, K. Wen, A. Rodrigues, and K. Bergman. 2017. Optical interconnects for extreme scale computing systems. Parallel Comput. 64 (May 2017), 65--80.
[155]
G. Salton and C. Buckley. 1988. Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24, 5 (Aug. 1988), 513--523.
[156]
V. Sarkar, W. Harrod, and A. E. Snavely. 2009. Software challenges in extreme scale systems. In Journal of Physics: Conference Series, Vol. 180. IOP Publishing, 012045.
[157]
O. Sarood, E. Meneses, and L. V. Kale. 2013. A “cool” way of improving the reliability of HPC machines. In SC’13: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. 1--12.
[158]
O. Sarood, P. Miller, E. Totoni, and L. V. Kalé. 2012. “Cool” load balancing for high performance computing data centers. IEEE Trans. Comput. 61, 12 (Dec. 2012), 1752--1764.
[159]
K. Sato, N. Maruyama, K. Mohror, A. Moody, T. Gamblin, B. R. De Supinski, and S. Matsuoka. 2012. Design and modeling of a non-blocking checkpointing system. In International Conference for High Performance Computing, Networking, Storage and Analysis, SC.
[160]
M. J. Schulte, M. Ignatowski, G. H. Loh, B. M. Beckmann, W. C. Brantley, S. Gurumurthi, N. Jayasena, I. Paul, S. K. Reinhardt, and G. Rodgers. 2015. Achieving exascale capabilities through heterogeneous computing. IEEE Micro 35, 4 (July 2015), 26--36.
[161]
F. Shahnaz, M. W. Berry, V. P. Pauca, and R. J. Plemmons. 2006. Document clustering using nonnegative matrix factorization. Information Processing 8 Management 42, 2 (March 2006), 373--386.
[162]
J. Shalf, S. Dosanjh, and J. Morrison. 2010. Exascale computing technology challenges. In Proceedings of the International Conference on High Performance Computing for Computational Science. Springer, 1--25.
[163]
J. Shalf, D. Quinlan, and C. Janssen. 2011. Rethinking hardware-software codesign for exascale systems. Computer 44, 11 (Nov. 2011), 22--30.
[164]
H. Shoukourian, T. Wilde, A. Auweter, and A. Bode. 2014. Monitoring power data: A first step towards a unified energy efficiency evaluation toolset for HPC data centers. Environmental Modelling 8 Software 56 (June 2014), 13--26.
[165]
A. Sidorova, N. Evangelopoulos, J. S. Valacich, and T. Ramakrishnan. 2008. Uncovering the intellectual core of the information systems discipline. MIS Quarterly 32, 3 (2008), 467--482.
[166]
M. Snir et al. 2014. Addressing failures in exascale computing. The International Journal of High Performance Computing Applications 28, 2 (May 2014), 129--173.
[167]
D. Stroobandt et al. 2016. EXTRA: Towards the exploitation of eXascale technology for reconfigurable architectures. In Proceedings of the 2016 11th International Symposium on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC). 1--7.
[168]
V. Subotić, S. Brinkmann, V. Marjanović, R. M. Badia, J. Gracia, C. Niethammer, E. Ayguade, J. Labarta, and M. Valero. 2013. Programmability and portability for exascale: Top down programming methodology and tools with StarSs. Journal of Computational Science 4, 6 (Nov. 2013), 450--456.
[169]
S. Syed and C. T. Weber. 2018. Using machine learning to uncover latent research topics in fishery models. Reviews in Fisheries Science 8 Aquaculture 26, 3 (July 2018), 319--336.
[170]
C. A. Thraskias, E. N. Lallas, N. Neumann, L. Schares, B. J. Offrein, R. Henker, D. Plettemeier, F. Ellinger, J. Leuthold, and I. Tomkos. 2018. Survey of photonic and plasmonic interconnect technologies for intra-datacenter and high-performance computing communications. IEEE Communications Surveys Tutorials 20, 4 (Fourthquarter 2018), 2758--2783.
[171]
E. Totoni, N. Jain, and L. V. Kalé. 2013. Toward runtime power management of exascale networks by on/off control of links. In Proceedings of the 2013 IEEE International Symposium on Parallel Distributed Processing, Workshops and Ph.D Forum. 915--922.
[172]
R. Trobec, R. Vasiljević, M. Tomašević, V. Milutinović, R. Beivide, and M. Valero. 2016. Interconnection networks in petascale computer systems: A survey. ACM Comput. Surv. 49, 3 (Sept. 2016), 44:1--44:24.
[173]
D. Unat, C. Chan, W. Zhang, S. Williams, J. Bachan, J. Bell, and J. Shalf. 2015. ExaSAT: An exascale co-design tool for performance modeling. The International Journal of High Performance Computing Applications 29, 2 (May 2015), 209--232.
[174]
L. van der Maaten and G. Hinton. 2008. Visualizing data using T-SNE. Journal of Machine Learning Research 9, (Nov. 2008), 2579--2605.
[175]
R. F. Van der Wijngaart, A. Kayi, J. R. Hammond, G. Jost, T. St. John, S. Sridharan, T. G. Mattson, J. Abercrombie, and J. Nelson. 2016. Comparing runtime systems with exascale ambitions using the parallel research kernels. In High Performance Computing (Lecture Notes in Computer Science). Springer International Publishing, 321--339.
[176]
A. Varghese, B. Edwards, G. Mitra, and A. P. Rendell. 2014. Programming the Adapteva Epiphany 64-core network-on-chip coprocessor. In Proceedings of the 2014 IEEE International Parallel Distributed Processing Symposium Workshops. 984--992.
[177]
M. Vázquez, G. Houzeaux, S. Koric, A. Artigues, J. Aguado-Sierra, R. Arŕs, D. Mira, H. Calmet, F. Cucchietti, H. Owen, A. Taha, E. D. Burness, J. M. Cela, and M. Valero. 2016. Alya: Multiphysics engineering simulation toward exascale. Journal of Computational Science 14 (May 2016), 15--27.
[178]
J. S. Vetter and S. Mittal. 2015. Opportunities for nonvolatile memory systems in extreme-scale high-performance computing. Computing in Science Engineering 17, 2 (March 2015), 73--82.
[179]
O. Villa, D. R. Johnson, M. Oconnor, E. Bolotin, D. Nellans, J. Luitjens, N. Sakharnykh, P. Wang, P. Micikevicius, A. Scudiero, S. W. Keckler, and W. J. Dally. 2014. Scaling the power wall: A path to exascale. In SC’14: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 830--841.
[180]
Y. Wang and Y. Zhang. 2013. Nonnegative matrix factorization: A comprehensive review. IEEE Transactions on Knowledge and Data Engineering 25, 6 (June 2013), 1336--1353.
[181]
C. Weinhold, A. Lackorzynski, J. Bierbaum, M. Küttler, M. Planeta, H. Härtig, A. Shiloh, E. Levy, T. Ben-Nun, A. Barak, T. Steinke, T. Schütt, J. Fajerski, A. Reinefeld, M. Lieber, and W. E. Nagel. 2016. FFMK: A fast and fault-tolerant microkernel-based system for exascale computing. In Software for Exascale Computing - SPPEXA 2013-2015 (Lecture Notes in Computational Science and Engineering). Springer International Publishing, 405--426.
[182]
S. Werner, J. Navaridas, and M. Luján. 2017. A survey on optical network-on-chip architectures. Comput. Surveys 50, 6 (Dec. 2017), 1--37.
[183]
W. Xu, X. Liu, and Y. Gong. 2003. Document clustering based on non-negative matrix factorization. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval (SIGIR’03). ACM, New York, NY, USA, 267--273.
[184]
C. Yang, J. C. Pichel, A. R. Smith, and D. A. Padua. 2014. Hierarchically tiled array as a high-level abstraction for codelets. In Proceedings of the 2014 Fourt Workshop on Data-Flow Execution Models for Extreme Scale Computing. IEEE, Edmonton, AB, Canada, 58--65.
[185]
H. Yu, C. Wang, R. W. Grout, J. H. Chen, and K. Ma. 2010. In situ visualization for large-scale combustion simulations. IEEE Computer Graphics and Applications 30, 3 (May 2010), 45--57.
[186]
M. Zakarya and L. Gillam. 2017. Energy efficient computing, clusters, grids and clouds: A taxonomy and survey. Sustainable Computing: Informatics and Systems 14 (June 2017), 13--33.
[187]
G. Zheng, X. Ni, and L. V. Kalé. 2012. A scalable double in-memory checkpoint and restart scheme towards exascale. In Proceedings of the IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN 2012). 1--6.
[188]
Q. Zheng, K. Ren, G. Gibson, B. W. Settlemyer, and G. Grider. 2015. DeltaFS: Exascale file systems scale better without dedicated servers. In Proceedings of the 10th Parallel Data Storage Workshop (PDSW’15). ACM, Austin, Texas, 1--6.
[189]
J. A. Zounmevo, S. Perarnau, K. Iskra, K. Yoshii, R. Gioiosa, B. C. V. Essen, M. B. Gokhale, and E. A. Leon. 2015. A container-based approach to OS specialization for exascale computing. In Proceedings of the 2015 IEEE International Conference on Cloud Engineering. 359--364.

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  1. The Landscape of Exascale Research: A Data-Driven Literature Analysis

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          cover image ACM Computing Surveys
          ACM Computing Surveys  Volume 53, Issue 2
          March 2021
          848 pages
          ISSN:0360-0300
          EISSN:1557-7341
          DOI:10.1145/3388460
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          Published: 20 March 2020
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