Ghalsasi, Prerana Prakash, authorRajopadhye, Sanjay, advisorBohm, Wim, committee memberPasricha, Sudeep, committee member2019-06-142019-06-142019https://hdl.handle.net/10217/195413Max-Plus algebra finds its applications in discrete event simulations, dynamic programming, biological sequence comparisons etc. Although there exist highly tuned libraries like CUDA Linear Algebra Subprograms (CuBLAS) [1] for matrix operations, they implement the standard matrix-multiplication (multiply-add) for floating points. We found no standard library for Max- Plus-Matrix-Multiplication (MPMM) on integers. Hence,we developed a highly tuned parallelized MPMM library kernel. We chose GPUs as hardware platform for this work because of their significantly more parallelism and arithmetic functional units as compared to CPUs. We designed this kernel to be portable across three successive Nvidia GPU architectures and it achieves performance in the range 3065 GOPs/S - 3631 GOPs/S on all of these architectures. We closely followed the benchmarking approach described by Volkov et al. [2] when they contributed to cuBLAS. This MPMM kernel can be part of a max-plus algebra library for GPUs and can help speed up Biological Sequence comparison applications like BPMax.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.matrix multiplicationtropical algebramax-plus algebraGPUsText