4 research outputs found
PolyDL: Polyhedral Optimizations for Creation of High Performance DL primitives
Deep Neural Networks (DNNs) have revolutionized many aspects of our lives.
The use of DNNs is becoming ubiquitous including in softwares for image
recognition, speech recognition, speech synthesis, language translation, to
name a few. he training of DNN architectures however is computationally
expensive. Once the model is created, its use in the intended application - the
inference task, is computationally heavy too and the inference needs to be fast
for real time use. For obtaining high performance today, the code of Deep
Learning (DL) primitives optimized for specific architectures by expert
programmers exposed via libraries is the norm. However, given the constant
emergence of new DNN architectures, creating hand optimized code is expensive,
slow and is not scalable.
To address this performance-productivity challenge, in this paper we present
compiler algorithms to automatically generate high performance implementations
of DL primitives that closely match the performance of hand optimized
libraries. We develop novel data reuse analysis algorithms using the polyhedral
model to derive efficient execution schedules automatically. In addition,
because most DL primitives use some variant of matrix multiplication at their
core, we develop a flexible framework where it is possible to plug in library
implementations of the same in lieu of a subset of the loops. We show that such
a hybrid compiler plus a minimal library-use approach results in
state-of-the-art performance. We develop compiler algorithms to also perform
operator fusions that reduce data movement through the memory hierarchy of the
computer system.Comment: arXiv admin note: substantial text overlap with arXiv:2002.0214