Automatically Harnessing Sparse Acceleration. Sparse linear algebra is central to many scientific programs, yet compilers fail to optimize it well. Highperformance libraries are available, but adoption costs are significant. Moreover, libraries tie programs into vendorspecific software and hardware ecosystems, creating nonportable code.  :38,this paper, we develop a new approach based on our specification Language for implementers of Linear Algebra Computations (LiLAC). Rather than requiring the application developer to (re)write every program for a given library, the burden is shifted to a oneoff description by the library implementer. The LiLACenabled compiler uses :38,this to insert appropriate library routines without source code changes.  :98,provides automatic data marshaling, maintaining state between calls and minimizing data transfers. Appropriate places for library insertion are detected in compiler intermediate representation, independent of source languages.  :126,evaluated on largescale scientific applications written in FORTRAN; standard CC and FORTRAN benchmarks; and C graph analytics kernels. Across heterogeneous platforms, applications and data sets we show speedups of 1.1times to over 10times without user intervention.

spECK: Accelerating GPU Sparse MatrixMatrix Multiplication Through Lightweight Analysis Sparse general matrixmatrix multiplication on GPUs is challenging due to the varying sparsity patterns of sparse matrices. Existing solutions achieve good performance for certain types of matrices, but fail to accelerate all kinds of matrices in the same manner. Our approach combines multiple strategies with dynamic parameter selection to dynamically choose and tune the best fitting algorithm for each row of the matrix. This choice is supported by a lightweight, multilevel matrix analysis, which carefully balances between analysis cost and expected performance gains. Our evaluation on thousands of matrices with various characteristics shows that we outperform all existing solutions in 85% of the cases and that we achieve the second best performance in 12%. Over the entire data set, our solution is on average twice as fast as the second best approach and up to 25times faster than other stateoftheart GPU implementations. Using our approach, applications can expect the best performance independent of the matrices they work on.NMathias PargerGraz University of TechnologyMartin WinterGraz University of Technology, AustriaDaniel Mlakar