  Sparse linear algebra is central to many scientific programs, yet compilers
fail to optimize it well. High-performance libraries are available, but
adoption costs are significant. Moreover, libraries tie programs into
vendor-specific software and hardware ecosystems, creating non-portable code.
  In 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 one-off description by the library implementer. The
LiLAC-enabled compiler uses this to insert appropriate library routines without
source code changes.
  LiLAC 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.
  We evaluated on large-scale scientific applications written in FORTRAN;
standard C/C++ and FORTRAN benchmarks; and C++ graph analytics kernels. Across
heterogeneous platforms, applications and data sets we show speedups of
1.1$\times$ to over 10$\times$ without user intervention.
