• A Latency Tolerant Hybrid Sparse Solver Using Incomplete Cholesky Factorization, P. Raghavan.
• Jun Doi and Yasushi Negishi. Overlapping Methods of All-to-All Communication and FFT Algorithms for Torus-Connected Massively Parallel Supercomputers, SC10.

• Green: a framework for supporting energy-conscious programming using controlled approximation, W. Baek and T. M. Chilimbi., PLDI '10, pp. 198-209 (2010).
• Techniques for multicore thermal management: Classification and new exploration, J. Donald and M. Martonosi. ACM SIGARCH Computer Architecture News 34(2):78-88 (2006).
• Studying the impact of application-level optimizations on the power consumption of multi-core architectures, Rahman, Shah Mohammad Faizur, et al. Proc. 9th Conf Computing Frontiers., 2012.
• Conservation cores: reducing the energy of mature computations, G. Venkatesh et al. , Proc. ASPLOS XV, pp. 205-218 (2010).
• Perfect strong scaling using no additional energy, J. Demmel et al., EECS Department University of California, Berkeley, Tech. Report No. UCB/EECS-2012-126 (2012).
• Instrumenting Linear Algebra Energy Consumption via On-chip Energy Counters, James Demmel and Andrew Gearhart, UCB/EECS-2012-168 June 2012)

Sparse Matrices 

• Model-driven autotuning of sparse matrix-vector multiply on GPUs, J. Choi, A. Singh and R. W. Vuduc. Proc. 15th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP '10),

Graph Algorithms  

• Efficient Parallel Graph Exploration on Multi-Core CPU and GPU, S. Hong, T. Oguntebi and K. Olukotun, 2011 Intl Conf Parallel Architectures and Compilation Techniques (PACT), Oct 2011, pp. 78-88.
• Pregel: a system for large-scale graph processing, G. Malewicz et al., Proc 2010 ACM SIGMOD Intl Conf Management of Data (SIGMOD '10).
• Challenges in Parallel Graph Processing, Andrew Lumsdaine et al. Parallel Process. Lett. 17(5) (2007)
• Book: Combinatorial Scientific Computing (Chapman & Hall/CRC Computational Science), 2012
• "Snap: Stanford network analysis project" http://snap.stanford.edu
• A Scalable Distributed Parallel Breadth-First Search Algorithm on BlueGene/L A. Yoo et al. Supercomputing '05
• Berkhin, Pavel. "A survey of clustering data mining techniques." Grouping multidimensional data (2006): 25-71. [1435 citations]
• A. Clauset, M. E. J. Newman, and C. Moore, "Finding Community Structure in Very Large Networks," Physical Review E, 6(70): 066111, December 2004.
• Girvan, Michelle, and Mark EJ Newman. "Community structure in social and biological networks." Proceedings of the National Academy of Sciences 99 (12) 7821-7826 (2002). [4143 citations]
• M. E. J. Newman, "Fast algorithm for detecting community structure in networks," Phys. Rev. E 69, 066133 (2004) [5 pages, 1584 citations]
• Data Mining: Concepts and Techniques, Jiawei Han, Micheline Kamber

• Exascale computing study: technology challenges in achieving exascale systems, by P. Kogge et al, September 2008, DARPA, 297 pp. The "Kogge report" is an encyclopedic source source of information with many references.
• "Software challenges in extreme scale systems," V. Sarkar, W. Harrod and A. E. Snavely. J. of Physics: Conference Series, 180(1):012045 (2009). IOP Publishing. A good overview of the software issues. For more details see the report, the next reading.
• "Exascale software study: Software challenges in extreme scale systems," V. Sarkar et al, DARPA IPTO, Air Force Research Labs, Tech. Rep, 159 pp. (2009).
• The International Exascale Software Project roadmap, by J. Dongarra et al., Intl J. of High Performance Computing Applications 25(1):3-60, February 2011.
• International Technology Roadmap for Semiconductors web site
• Rigel: an architecture and scalable programming interface for a 1000-core accelerator, J. H. Kelm, et al., Proc 36th Intl Symp Computer architecture (ISCA '09), pp.140-151.
• A framework for adaptive algorithm selection in STAPL, N. Thomas et al. PPoPP '05, pp. 277-288.
• Coordinated management of multiple interacting resources in chip multiprocessors: A machine learning approach, R. Bitirgen, E. Ipek and J. F. Martinez. Proc. 41st annual IEEE/ACM International Symposium on Microarchitecture, pp. 318-329 (2008).
• Self-adaptive software: Landscape and research challenges, M. Salehie and L. Tahvildari, ACM Trans. Auton. Adapt. Syst. 4(2), Article 14 (May 2009), 42 pages.

• K. H. Huang and A. J. Abraham, "Algorithm Based Fault Tolerant for Matrix Operations.,", IEEE Trans on Comput., C33:518-528, 1984. Seminal paper describing ABFT technique to detect and correct errors in dense matrix computations using checksum-encoded matrices. They use the linear transformation property of matrix computations to detect and correct a single error.
• Fault-tolerant matrix arithmetic and signal processing on highly concurrent computing structures , J-Y. Jou and J. A. Abraham, Proc IEEE 74(5):732- 741, May 1986
• Predicting the number of fatal soft errors in Los Alamos national laboratory's ASC Q supercomputer, S. E. Michalak et al., IEEE Trans. Device and Materials Reliability, 5(3):329-335, Sept. 2005.
• Algorithm-Based Fault Tolerance for Fail-Stop Failures, Zizhong Chen and Jack Dongarra, IEEE Trans. Parallel Distrib. Syst. 19(12):1628-1641 (December 2008). Shows why only the outer product keeps the consistency between the matrix content and the checksum during the computations.
• D. Peng, A. Bouteiller, G. Bosilca, T. Herault and J. Dongarra. Algorithm-based fault tolerance for dense matrix factorizations, SIGPLAN Not. 47(8):225-234 (February 2012).
• ERSA: error resilient system architecture for probabilistic applications, L. Larkhoon et al, Conference Design, Automation and Test in Europe (DATE '10), pp. 1560-1565.

• To find additional readings, consult the Bebop web site (UC Berkeley) or Jim Demmel’s Course on Communication-Avoiding algorithms.
• Graph expansion and communication costs of fast matrix multiplication, G. Ballard, J. Demmel et al. , Proc. 23rd ACM symposium on Parallelism in algorithms and architectures (SPAA '11), pp 1-12, 2011.
• Communication-Optimal Parallel Algorithm for Strassen's Matrix Multiplication, G. Ballard, J. Demmel, et al., Feb. 2012.
• Communication Avoiding Gaussian Elimination, Supercomputing 2008, November 2008), Laura Grigori, James Demmel, and Hua Xiang.