@Article{Dalton:2015:OSM,
  author =       "Steven Dalton and Luke Olson and Nathan Bell",
  title =        "Optimizing Sparse Matrix-Matrix Multiplication 
                  for the {GPU}",
  journal =      "{ACM} Transactions on Mathematical Software",
  volume =       "41",
  number =       "4",
  accepted =     "1 November 2014",
  upcoming =     "true", 
  abstract =     "
                  Sparse matrix-matrix multiplication
                  (SpGEMM) is a key operation in numerous
                  areas from information to the physical
                  sciences. Implementing SpGEMM efficiently
                  on throughput-oriented processors,
                  such as the graphics processing unit
                  (GPU), requires the programmer to expose
                  substantial fine-grained parallelism
                  while conserving the limited off-chip
                  memory bandwidth. Balancing these
                  concerns, we decompose the SpGEMM
                  operation into three, highly-parallel
                  phases: expansion, sorting, and
                  contraction, and introduce a set
                  of complementary bandwidth-saving
                  performance optimizations. Our
                  implementation is fully general and

                  our optimization strategy adaptively
                  processes the SpGEMM workload row-wise
                  to substantially improve the performance
                  by decreasing the work complexity and
                  utilizing the memory hierarchy more
                  effectively.",
}