Abstract
This chapter shows several new programming strategies based on tasking to parallelize sparse linear algebra kernels. The reader will explore different approaches to improve the performance of these kernels thanks to a better workload distribution and comprehension of the data layout. This will be accomplished through the study of some of the most popular and widely used sparse operations, such as SpMV (sparse matrix vector multiplication), GTSV (triangular solve), or CG (conjugate gradient). Those strategies have been tested on multicore systems. Some of them equipped GPU devices, showcasing how to overcome the peculiarities of task-based parallelized kernels in the context of sparse linear algebra computations.
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