  Sparse computations frequently appear in scientific simulations and the
performance of these simulations rely heavily on the optimization of the sparse
codes. The compact data structures and irregular computation patterns in sparse
matrix computations introduce challenges to vectorizing these codes. Available
approaches primarily vectorize regular regions of computations in the sparse
code. They also reorganize data and computations, at a cost, to increase the
number of regular regions. In this work, we propose a novel polyhedral model,
called the partially strided codelets (PSC), that enables the vectorization of
computation regions with irregular data access patterns. PSCs also improve data
locality in sparse computation. Our DDF inspector-executor framework
efficiently mines the memory accesses in the sparse computation, using an
access function differentiation approach, to find PSC codelets. It generates
vectorized code for the sparse matrix multiplication kernel (SpMV), a kernel
with parallel outer loops, and for kernels with carried dependence,
specifically the sparse triangular solver (SpTRSV). We demonstrate the
performance of the DDF-generated code on a set of 60 large and small matrices
(0.05-130M nonzeros). DDF outperforms the highly specialized library MKL with
an average speedup of 1.93 and 4.5X for SpMV and SpTRSV, respectively. For the
same matrices, DDF outperforms the state-of-the-art inspector-executor
framework Sympiler [1] for the SpTRSV kernel by up to 11X and the work by
Augustine et. al [2] for the SpMV kernel by up to 12X.
