Séminaire Cambium, Inria Paris, 2 rue Simone Iff
                          Salle Lions 1, bâtiment C
                         Vendredi 9 septembre, 10h00

                            Jonathan Ragan-Kelley
                                     MIT

                Safe & Productive High-Performance Programming
                       with User-Schedulable Languages

In the twilight of Moore’s law, computational performance is limited
by locality (the distance over which data has to move, e.g., from
nearby caches, far away main memory, or across networks), parallelism,
and hardware specialization. Because of this, I argue that we should
think of the performance and efficiency of an application as
determined not just by the algorithm and the hardware on which it
runs, but critically also by the organization of its computations and
data. For algorithms with the same complexity—even the exact same set
of arithmetic operations—the order, granularity, and placement of
computations and data can easily change performance by an order of
magnitude because of locality, parallelism, and hardware
specialization. Because explicit control over the organization of
computation is below the level of abstraction of common high-level
languages, high-performance code traditionally has to be written at
much greater cost in low-level languages like C/C++, with safety and
correctness also offloaded to the programmer.

My group has been working to develop a new paradigm for
high-performance programming—user-schedulable languages—where the
specification of computation is decoupled from choices about its
organization, but the organization is still controlled explicitly
through “scheduling directives.” The compiler guarantees the safety
and correctness of the program after scheduling, eliminating a large
class of bugs and significantly improving programmer productivity,
while still enabling performance engineers to deliver state-of-the-art
performance on complex modern hardware. In this talk, I will introduce
user-schedulable languages through the example of Halide, a
high-performance language we developed for image and tensor
processing, and conclude by discussing new directions including richer
support for specialized hardware and end-to-end verified compilation.


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