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Towards Compositional and Generative Tensor optimizations 2017

Towards Compositional and Generative Tensor Optimizations

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16th ACM SIGPLAN International Conference on Generative Programming - Concepts and Experiences (GPCE) co-located with SPLASH Conference

作者： Susungi, Adilla Rink, Norman A. Castrillon, Jeronimo Huismann, Immo Cohen, Albert Tadonki, Claude Stiller, Joerg Froehlich, Jochen PSL Res Univ MINES ParisTech Paris France Tech Univ Dresden Chair Compiler Construct Dresden Germany Tech Univ Dresden Chair Fluid Mech Dresden Germany Ecole Normale Super INRIA Paris France

ISBN: (纸本)9781450355247

Many numerical algorithms are naturally expressed as operations on tensors (i.e. multi-dimensional arrays). Hence, tensor expressions occur in a wide range of application domains, e.g. quantum chemistry and physics;big data analysis and machine learning;and computational fluid dynamics. Each domain, typically, has developed its own strategies for efficiently generating optimized code, supported by tools such as domain-specific languages, compilers, and libraries. However, strategies and tools are rarely portable between domains, and generic solutions typically act as "black boxes" that offer little control over code generation and optimization. As a consequence, there are application domains without adequate support for easily generating optimized code, e.g. computational fluid dynamics. In this paper we propose a generic and easily extensible intermediate language for expressing tensor computations and code transformations in a modular and generative fashion. Beyond being an intermediate language, our solution also offers meta-programming capabilities for experts in code optimization. While applications from the domain of computational fluid dynamics serve to illustrate our proposed solution, we believe that our general approach can help unify research in tensor optimizations and make solutions more portable between domains.

关键词： numerical methods computational fluid dynamics (CFD) tensor methods intermediate language meta-programming code generation and optimization

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CFDlang: High-level code generation for high-order methods in fluid dynamics 2018

CFDlang: High-level code generation for high-order methods i...

引用

3rd ACM International Workshop on Real World Domain Specific Languages (RWDSL)

作者： Rink, Norman A. Huismann, Immo Susungi, Adilla Castrillon, Jeronimo Stiller, Joerg Froehlich, Jochen Tadonki, Claude Tech Univ Dresden Dresden Germany PSL Res Univ MINES ParisTech Paris France

ISBN: (纸本)9781450363556

Numerical simulations continue to enable fast and enormous progress in science and engineering. Writing efficient numerical codes is a difficult challenge that encompasses a variety of tasks from designing the right algorithms to exploiting the full potential of a platform's architecture. Domain-specific languages (DSLs) can ease these tasks by offering the right abstractions for expressing numerical problems. With the aid of domain knowledge, efficient code can then be generated automatically from abstract expressions. In this work, we present the CFDlang DSL for expressing tensor operations that constitute the performance-critical code sections in a class of real numerical applications from fluid dynamics. We demonstrate that CFDlang can be used to generate code automatically that performs as well, if not better, than carefully hand-optimized code.

关键词： language (DSL) design code generation and optimization tensor expressions numerical methods computational fluid dynamics (CFD)

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oneDNN Graph Compiler: A Hybrid Approach for High-Performance Deep Learning Compilation 24

oneDNN Graph Compiler: A Hybrid Approach for High-Performanc...

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22nd IEEE/ACM International Symposium on code generation and optimization (CGO)

作者： Li, Jianhui Qin, Zhennan Mei, Yijie Cui, Jingze Song, Yunfei Chen, Ciyong Zhang, Yifei Du, Longsheng Cheng, Xianhang Jin, Baihui Zhang, Yan Ye, Jason Lin, Eric Lavery, Dan Intel Santa Clara CA 95051 USA Intel Beijing Peoples R China

ISBN: (纸本)9798350395099

With the rapid development of deep learning models and hardware support for dense computing, the deep learning (DL) workload characteristics changed significantly from a few hot spots on compute-intensive operations to a broad range of operations scattered across the models. Accelerating a few compute-intensive operations using the expert-tuned implementation of primitives doesn't fully exploit the performance potential of AI hardware. Various efforts have been made to compile a full deep neural network (DNN) graph. One of the biggest challenges is to achieve high-performance tensor compilation by generating expert-level performance code for the dense compute-intensive operations and applying compilation optimization at the scope of DNN computation graph across multiple compute-intensive operations. We present oneDNN Graph Compiler, a tensor compiler that employs a hybrid approach of using techniques from both compiler optimization and expert-tuned kernels for highperformance code generation of the deep neural network graph. oneDNN Graph Compiler addresses unique optimization challenges in the deep learning domain, such as low-precision computation, aggressive fusion of graph operations, optimization for static tensor shapes and memory layout, constant weight optimization, and memory buffer reuse. Experimental results demonstrate significant performance gains over existing tensor compiler and primitives library for performance-critical DNN computation graphs and end-to-end models on Intel (R) Xeon (R) Scalable Processors.

关键词： Deep Learning Compiler code generation and optimization High-Performance Library

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Unbounded Superoptimization

Unbounded Superoptimization

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ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward) part of SPLASH Conference

作者： Jangda, Abhinav Yorsh, Greta Indian Inst Technol BHU Varanasi Uttar Pradesh India Queen Mary Univ London London England

ISBN: (纸本)9781450355308

Our aim is to enable software to take full advantage of the capabilities of emerging microprocessor designs without modifying the compiler. Towards this end, we propose a new approach to code generation and optimization. Our approach uses an SMT solver in a novel way to generate efficient code for modern architectures and guarantee that the generated code correctly implements the source code. The distinguishing characteristic of our approach is that the size of the constraints does not depend on the candidate sequence of instructions. To study the feasibility of our approach, we implemented a preliminary prototype, which takes as input LLVM IR code and uses Z3 SMT solver to generate ARMv7-A assembly. The prototype handles arbitrary loop-free code (not only basic blocks) as input and output. We applied it to small but tricky examples used as standard benchmarks for other superoptimization and synthesis tools. We are encouraged to see that Z3 successfully solved complex constraints that arise from our approach. This work paves the way to employing recent advances in SMT solvers and has a potential to advance SMT solvers further by providing a new category of challenging benchmarks that come from an industrial application domain.

关键词： superoptimization software synthesis code generation and optimization SMT solver constraint solvers first order logic instruction set architecture

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Instruction re-selection for iterative modulo scheduling on high performance multi-issue DSPs

Instruction re-selection for iterative modulo scheduling on ...

引用

EUC 2006 Workshops

作者： Cho, Doosan Ravi, Ayyagari Uh, Gang-Ryung Paek, Yunheung Seoul Natl Univ Sch Elect Engn & Comp Sci Seoul 151744 South Korea Boise State Univ Dept Comp Sci Boise ID 83725 USA Seoul Natl Univ Ctr SoC Design Technol Seoul South Korea

ISBN: (纸本)3540368507

An iterative modulo scheduling is very important for compilers targeting high performance multi-issue digital signal processors. This is because these processors are often severely limited by idle state functional units and thus the reduced idle units can have a positively significant impact on their performance. However, complex instructions, which are used in most recent DSPs such as mac, usually increase data dependence complexity, and such complex dependencies that exist in signal processing applications often restrict modulo scheduling freedom and therefore, become a limiting factor of the iterative modulo scheduler. In this work, we propose a technique that efficiently reselects instructions of an application loop code considering dependence complexity, which directly resolve the dependence constraint. That is specifically featured for accelerating software pipelining performance by minimizing length of intrinsic cyclic dependencies. To take advantage of this feature, few existing compilers support a loop unrolling based dependence relaxing technique, but only use them for some limited cases. This is mainly because the loop unrolling typically occurs an overhead of huge code size increment, and the iterative modulo scheduling with relaxed dependence techniques for general cases is an NP-hard problem that necessitates complex assignments of registers and functional units. Our technique uses a heuristic to efficiently handle this problem in pre-stage of iterative modulo scheduling without loop unrolling.

关键词： code generation and optimization application specific embedded software design software pipelining dependence analysis high performance DSPs

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Meta-programming for Cross-Domain Tensor optimizations 2018

Meta-programming for Cross-Domain Tensor Optimizations

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17th ACM SIGPLAN International Conference on Generative Programming - Concepts and Experiences (GPCE) part of the SPLASH Collection of Conferences and Workshops

作者： Susungi, Adilla Rink, Norman A. Cohen, Albert Castrillon, Jeronimo Tadonki, Claude PSL Res Univ MINES ParisTech Paris France Tech Univ Dresden Chair Compiler Construct Dresden Germany INRIA Villers Les Nancy France ENS DI Paris France

ISBN: (纸本)9781450360456

Many modern application domains crucially rely on tensor operations. The optimization of programs that operate on tensors poses difficulties that are not adequately addressed by existing languages and tools. Frameworks such as TensorFlow offer good abstractions for tensor operations, but target a specific domain, i.e. machine learning, and their optimization strategies cannot easily be adjusted to other domains. General-purpose optimization tools such as Pluto and existing meta-languages offer more flexibility in applying optimizations but lack abstractions for tensors. This work closes the gap between domain-specific tensor languages and general-purpose optimization tools by proposing the Tensor optimizations Meta-Language (TEML). Ton offers high-level abstractions for both tensor operations and loop transformations, and enables flexible composition of transformations into effective optimization paths. This compositionality is built into TEML's design, as our formal language specification will reveal. We also show that TEML can express tensor computations as comfortably as TensorFlow and that it can reproduce Pluto's optimization paths. Thus, optimized programs generated by TEML execute at least as fast as the corresponding Pluto programs. In addition, TEML enables optimization paths that often allow outperforming Pluto.

关键词： tensor algebra meta-programming code generation and optimization denotational semantics

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Lifting code generation of Cardiac Physiology Simulation to Novel Compiler Technology 2023

Lifting Code Generation of Cardiac Physiology Simulation to ...

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21st ACM/IEEE International Symposium on code generation and optimization (CGO)

作者： Thangamani, Arun Jost, Tiago Trevisan Loechner, Vincent Genaud, Stephane Bramas, Berenger Strasbourg Univ Strasbourg France INRIA Paris France

ISBN: (纸本)9798400701016

The study of numerical models for the human body has become a major focus of the research community in biology and medicine. For instance, numerical ionic models of a complex organ, such as the heart, must be able to represent individual cells and their interconnections through ionic channels, forming a system with billions of cells, and requiring efficient code to handle such a large system. The modeling of the electrical system of the heart combines a compute-intensive kernel that calculates the intensity of current flowing through cell membranes, and feeds a linear solver for computing the electrical potential of each cell. Considering this context, we propose limpetMLIR, a code generator and compiler transformer to accelerate the kernel phase of ionic models and bridge the gap between compiler technology and electrophysiology simulation. LimpetMLIR makes use of the MLIR infrastructure, its dialects, and transformations to drive forward the study of ionic models, and accelerate the execution of multi-cell systems. Experiments conducted in 43 ionic models show that our limpetMLIR based code generation greatly outperforms current state-of-the-art simulation systems by an average of 2.9x, reaching peak speedups of more than 15x in some cases. To our knowledge, this is the first work that deeply connects an optimizing compiler infrastructure to electrophysiology models of the human body, showing the potential benefits of using compiler technology in the simulation of human cell interactions.

关键词： code generation and optimization code transformation domain-specific languages vectorization

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