Proceedings ArticleDOI
A practical automatic polyhedral parallelizer and locality optimizer
Uday Bondhugula,Albert Hartono,J. Ramanujam,P. Sadayappan +3 more
- Vol. 43, Iss: 6, pp 101-113
TLDR
An automatic polyhedral source-to-source transformation framework that can optimize regular programs for parallelism and locality simultaneously simultaneously and is implemented into a tool to automatically generate OpenMP parallel code from C program sections.Abstract:
We present the design and implementation of an automatic polyhedral source-to-source transformation framework that can optimize regular programs (sequences of possibly imperfectly nested loops) for parallelism and locality simultaneously. Through this work, we show the practicality of analytical model-driven automatic transformation in the polyhedral model -- far beyond what is possible by current production compilers. Unlike previous works, our approach is an end-to-end fully automatic one driven by an integer linear optimization framework that takes an explicit view of finding good ways of tiling for parallelism and locality using affine transformations. The framework has been implemented into a tool to automatically generate OpenMP parallel code from C program sections. Experimental results from the tool show very high speedups for local and parallel execution on multi-cores over state-of-the-art compiler frameworks from the research community as well as the best native production compilers. The system also enables the easy use of powerful empirical/iterative optimization for general arbitrarily nested loop sequences.read more
Citations
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Proceedings ArticleDOI
Memory-centric accelerator design for Convolutional Neural Networks
TL;DR: It is shown that the effects of the memory bottleneck can be reduced by a flexible memory hierarchy that supports the complex data access patterns in CNN workload and ensures that on-chip memory size is minimized, which reduces area and energy usage.
Journal ArticleDOI
Polyhedral parallel code generation for CUDA
Sven Verdoolaege,Juan Carlos Juega,Albert Cohen,José Ignacio Gómez,Christian Tenllado,Francky Catthoor +5 more
TL;DR: A novel source-to-source compiler called PPCG is presented, which introduces a multilevel tiling strategy and a code generation scheme for the parallelization and locality optimization of imperfectly nested loops, managing memory and exposing concurrency according to the constraints of modern GPUs.
Proceedings ArticleDOI
PATUS: A Code Generation and Autotuning Framework for Parallel Iterative Stencil Computations on Modern Microarchitectures
TL;DR: This work presents a code generation and auto-tuning framework for stencil computations targeted at multi- and many core processors, such as multicore CPUs and graphics processing units, which makes it possible to generate compute kernels from a specification of the stencil operation and a parallelization and optimization strategy, and leverages the auto tuning methodology to optimize strategy-dependent parameters for the given hardware architecture.
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Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
Nicolas Vasilache,Oleksandr Zinenko,Theodoros Theodoridis,Priya Goyal,Zachary DeVito,William S. Moses,Sven Verdoolaege,Andrew Adams,Albert Cohen +8 more
TL;DR: A language close to the mathematics of deep learning called Tensor Comprehensions offering both imperative and declarative styles, a polyhedral Just-In-Time compiler to convert a mathematical description of a deep learning DAG into a CUDA kernel with delegated memory management and synchronization, and a compilation cache populated by an autotuner are contributed.
Journal ArticleDOI
Polly — performing polyhedral optimizations on a low-level intermediate representation
TL;DR: Polly is presented, an infrastructure for polyhedral optimizations on the compiler's internal, low-level, intermediate representation (IR) and an interface for connecting external optimizers and a novel way of using the parallelism they introduce to generate SIMD and OpenMP code is presented.
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