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AutoFDO: Automatic feedback-directed optimization for warehouse-scale applications

AutoFDO: Automatic feedback-directed optimization for wareho...

引用

international symposium on code generation and optimization (CGO)

作者： Dehao Chen Tipp Moseley David Xinliang Li Google Inc.

ISBN: (纸本)9781509042456

AutoFDO is a system to simplify real-world deployment of feedback-directed optimization (FDO). The system works by sampling hardware performance monitors on production machines and using those profiles to guide optimization. Profile data is stale by design, and we have implemented compiler features to deliver stable speedup across releases. The resulting performance has a geometric mean improvement of 10.5. The system is deployed to hundreds of binaries at Google, and it is extremely easy to enable; users need only to add some flags to their release build. To date, AutoFDO has increased the number of FDO users at Google by 8X and has doubled the number of cycles spent in FDO-optimized binaries. Over half of CPU cycles used are now spent in some flavor of FDO-optimized binaries.

关键词： Position measurement Google optimization Databases Vegetation Bars

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Opening polyhedral compiler's black box

Opening polyhedral compiler's black box

引用

international symposium on code generation and optimization (CGO)

作者： Lénaïc Bagnères Oleksandr Zinenko Stéphane Huot Cédric Bastoul Inria and Univ. Paris-Saclay Orsay France

While compilers offer a fair trade-off between productivity and executable performance in single-threaded execution, their optimizations remain fragile when addressing compute-intensive code for parallel architectures with deep memory hierarchies. Moreover, these optimizations operate as black boxes, impenetrable for the user, leaving them with no alternative to time-consuming and error-prone manual optimization in cases where an imprecise cost model or a weak analysis resulted in a bad optimization decision. To address this issue, we propose a technique allowing to automatically translate an arbitrary polyhedral optimization, used internally by loop-level optimization frameworks of several modern compilers, into a sequence of comprehensible syntactic transformations as long as this optimization focuses on scheduling loop iterations. This approach opens the black box of the polyhedral frameworks, enabling users to examine, refine, replay and even design complex optimizations semi-automatically in partnership with the compiler.

关键词： optimization Syntactics Program processors Pluto Computational modeling Schedules Manuals

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Flexible on-stack replacement in LLVM

Flexible on-stack replacement in LLVM

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international symposium on code generation and optimization (CGO)

作者： Daniele Cono D'Elia Camil Demetrescu Dept. of Computer Control and Management Engineering Sapienza University of Rome Italy

ISBN: (纸本)9781509042456

On-Stack Replacement (OSR) is a technique for dynamically transferring execution between different versions of a function at run time. OSR is typically used in virtual machines to interrupt a long-running function and recompile it at a higher optimization level, or to replace it with a different one when a speculative assumption made during its compilation no longer holds. In this paper we present a framework for OSR that introduces novel ideas and combines features of existing techniques that no previous solution provided simultaneously. New features include OSR with compensation code to adjust the program state during a transition and the ability to fire an OSR from arbitrary locations in the code. Our approach is platform-independent as the OSR machinery is entirely encoded at a compiler's intermediate representation level. We implement and evaluate our technique in the LLVM compiler infrastructure, which is gaining popularity as Just-In-Time (JIT) compiler in virtual machines for dynamic languages such as Javascript, MATLAB, Python, and Ruby. As a case study of our approach, we show how to improve the state of the art in the optimization of the feval instruction, a performance-critical construct of the MATLAB language.

关键词： Instruments optimization MATLAB Machinery Virtual machining Libraries Registers

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Cheetah: Detecting false sharing efficiently and effectively

Cheetah: Detecting false sharing efficiently and effectively

引用

international symposium on code generation and optimization (CGO)

作者： Tongping Liu Xu Liu Department of Computer Science University of Texas at San Antonio San Antonio TX USA Department of Computer Science College of William and Mary Williamsburg VA USA

ISBN: (纸本)9781509042456

False sharing is a notorious performance problem that may occur in multithreaded programs when they are running on ubiquitous multicore hardware. It can dramatically degrade the performance by up to an order of magnitude, significantly hurting the scalability. Identifying false sharing in complex programs is challenging. Existing tools either incur significant performance overhead or do not provide adequate information to guide code optimization. To address these problems, we develop Cheetah, a profiler that detects false sharing both efficiently and effectively. Cheetah leverages the lightweight hardware performance monitoring units (PMUs) that are available in most modern CPU architectures to sample memory accesses. Cheetah develops the first approach to quantify the optimization potential of false sharing instances without actual fixes, based on the latency information collected by PMUs. Cheetah precisely reports false sharing and provides insightful optimization guidance for programmers, while adding less than 7% runtime overhead on average. Cheetah is ready for real deployment.

关键词： Instruction sets Hardware Phasor measurement units optimization Monitoring Multicore processing

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Re-constructing high-level information for language-specific binary re-optimization

Re-constructing high-level information for language-specific...

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international symposium on code generation and optimization (CGO)

作者： Toshihiko Koju Reid Copeland Motohiro Kawahito Moriyoshi Ohara IBM Research - Tokyo Japan IBM Canada Canada

ISBN: (纸本)9781509042456

In this paper, we show a binary optimizer can achieve competitive performance relative to a state-of-the-art source code compiler by re-constructing high-level information (HLI) from binaries. Recent advances in compiler technologies have resulted in a large performance gap between binaries compiled with old compilers and those compiled with latest ones. This motivated us to develop a binary optimizer for old binaries using a compiler engine for a latest source code compiler. However, a traditional approach to naively convert machine instructions into an intermediate representation (IR) of the compiler engine, does not allow us to take full advantage of optimization techniques available in the compiler. This is because the HLI, such as information about variables and their data types, is not available in such an IR. To address this issue, we have devised a technique to re-construct the HLI from binaries by using contextual information. This contextual information is a set of knowledge about specific compilation technologies, such as the conventions of data structures, the patterns of instruction sequences, and the semantics of runtime routines. With this technique, our binary optimizer has improved the performance of binaries generated from an older compiler by 40.1% on average in the CPU time for a set of benchmarks, which is close to the one due to a source-code recompilation with the same compiler engine, 55.2% on average.

关键词： Engines optimization Registers Runtime Program processors Semantics Binary codes

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Exploiting recent SIMD architectural advances for irregular applications

Exploiting recent SIMD architectural advances for irregular ...

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international symposium on code generation and optimization (CGO)

作者： Linchuan Chen Peng Jiang Gagan Agrawal The Ohio State University Columbus OH USA

ISBN: (纸本)9781509042456

A broad class of applications involve indirect or data-dependent memory accesses and are referred to as irregular applications. Recent developments in SIMD architectures - specifically, the emergence of wider SIMD lanes, combination of SIMD parallelism with many-core MIMD parallelism, and more flexible programming APIs - are providing new opportunities as well as challenges for this class of applications. In this paper, we propose a general optimization methodology, to effectively optimize different subclasses of irregular applications. Based on the observation that all applications with indirect memory accesses can be viewed as sparse matrix computations, we design an optimization methodology, which includes three sub-steps: 1) locality enhancement through tiling, 2) data access pattern identification, and 3) write conflict removal at both SIMD and MIMD levels. This method has been applied to unstructured grids, molecular dynamics, and graph applications, in addition to sparse matrix computations. The speedups achieved by our single threaded vectorized code over serial code is up to 9.05, whereas the overall speedup while utilizing both SIMD and MIMD (61 cores in Intel Xeon Phi) with our approach is up to 467.1. Further optimization using matrix reordering on irregular reductions and graph algorithms is able to achieve an incremental speedup of up to 1.69, though at a relatively high preprocessing cost. Moreover, SpMM using our approach outperforms routines from a highly optimized commercial library by up to 2.81×.

关键词： Sparse matrices Parallel processing Instruction sets Arrays Programming Hardware optimization

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A basic linear algebra compiler for structured matrices

A basic linear algebra compiler for structured matrices

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international symposium on code generation and optimization (CGO)

作者： Daniele G. Spampinato Markus Püschel Department of Computer Science ETH Zurich

ISBN: (纸本)9781509042456

Many problems in science and engineering are in practice modeled and solved through matrix computations. Often, the matrices involved have structure such as symmetric or triangular, which reduces the operations count needed to perform the computation. For example, dense linear systems of equations are solved by first converting to triangular form and optimization problems may yield matrices with any kind of structure. The well-known BLAS (basic linear algebra subroutine) interface provides a small set of structured matrix computations, chosen to serve a certain set of higher level functions (LAPACK). However, if a user encounters a computation or structure that is not supported, she loses the benefits of the structure and chooses a generic library. In this paper, we address this problem by providing a compiler that translates a given basic linear algebra computation on structured matrices into optimized C code, optionally vectorized with intrinsics. Our work combines prior work on the Spiral-like LGen compiler with techniques from polyhedral compilation to mathematically capture matrix structures. In the paper we consider upper/lower triangular and symmetric matrices but the approach is extensible to a much larger set including blocked structures. We run experiments on a modern Intel platform against the Intel MKL library and a baseline implementation showing competitive performance results for both BLAS and non-BLAS functionalities.

关键词： Symmetric matrices optimization Libraries Matrix decomposition Matrix converters DSL

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StructSlim: A lightweight profiler to guide structure splitting

StructSlim: A lightweight profiler to guide structure splitt...

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international symposium on code generation and optimization (CGO)

作者： Probir Roy Xu Liu Department of Computer Science College of William and Mary Williamsburg VA USA

ISBN: (纸本)9781509042456

Memory access latency continues to be a dominant bottleneck in a large class of applications on modern architectures. To optimize memory performance, it is important to utilize the locality in the memory hierarchy. Structure splitting can significantly improve memory locality. However, pinpointing inefficient code and providing insightful guidance for structure splitting is challenging. Existing tools typically leverage heavyweight memory instrumentations, which hinders the applicability of these tools for real long-running programs. To address this issue, we develop StructSlim, a profiler to pinpoint top candidates that benefit from structure splitting. StructSlim makes three unique contributions. First, it adopts lightweight address sampling to collect and analyze memory traces. Second, StructSlim employs a set of novel methods to determine memory access patterns to guide structure splitting. We also formally prove that our method has high accuracy even with sparse memory access samples. Third, StructSlim scales on multithreaded machines. StructSlim works on fully optimized, unmodified binary executables independently from their compiler and language, incurring around 7% runtime overhead. To evaluate StructSlim, we study seven sequential and parallel benchmarks. With the guidance of StructSlim, we are able to significantly improve all these benchmarks; the speedup is up to 1.37×.

关键词： Instruments optimization Monitoring Arrays Benchmark testing Program processors Bandwidth

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gpucc: An open-source GPGPU compiler

gpucc: An open-source GPGPU compiler

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international symposium on code generation and optimization (CGO)

作者： Jingyue Wu Artem Belevich Eli Bendersky Mark Heffernan Chris Leary Jacques Pienaar Bjarke Roune Rob Springer Xuetian Weng Robert Hundt Google Inc. USA

ISBN: (纸本)9781509042456

Graphics Processing Units have emerged as powerful accelerators for massively parallel, numerically intensive workloads. The two dominant software models for these devices are NVIDIA's CUDA and the cross-platform OpenCL standard. Until now, there has not been a fully open-source compiler targeting the CUDA environment, hampering general compiler and architecture research and making deployment difficult in datacenter or supercomputer environments. In this paper, we present gpucc, an LLVM-based, fully open-source, CUDA compatible compiler for high performance computing. It performs various general and CUDA-specific optimizations to generate high performance code. The Clang-based frontend supports modern language features such as those in C++11 and C++14. Compile time is 8% faster than NVIDIA's toolchain (nvcc) and it reduces compile time by up to 2.4x for pathological compilations (>100 secs), which tend to dominate build times in parallel build environments. Compared to nvcc, gpucc's runtime performance is on par for several open-source benchmarks, such as Rodinia (0.8% faster), SHOC (0.5% slower), or Tensor (3.7% faster). It outperforms nvcc on internal large-scale end-to-end benchmarks by up to 51.0%, with a geometric mean of 22.9%.

关键词： Graphics processing units Open source software Benchmark testing C++ languages optimization Generators Runtime

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Have abstraction and eat performance, too: Optimized heterogeneous computing with parallel patterns

Have abstraction and eat performance, too: Optimized heterog...

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international symposium on code generation and optimization (CGO)

作者： Kevin J. Brown HyoukJoong Lee Tiark Romp Arvind K. Sujeeth Christopher De Sa Christopher Aberger Kunle Olukotun Stanford University USA Google USA Purdue University System West Lafayette IN US

ISBN: (纸本)9781509042456

High performance in modern computing platforms requires programs to be parallel, distributed, and run on heterogeneous hardware. However programming such architectures is extremely difficult due to the need to implement the application using multiple programming models and combine them together in ad-hoc ways. To optimize distributed applications both for modern hardware and for modern programmers we need a programming model that is sufficiently expressive to support a variety of parallel applications, sufficiently performant to surpass hand-optimized sequential implementations, and sufficiently portable to support a variety of heterogeneous hardware. Unfortunately existing systems tend to fall short of these requirements. In this paper we introduce the Distributed Multiloop Language (DMLL), a new intermediate language based on common parallel patterns that captures the necessary semantic knowledge to efficiently target distributed heterogeneous architectures. We show straightforward analyses that determine what data to distribute based on its usage as well as powerful transformations of nested patterns that restructure computation to enable distribution and optimize for heterogeneous devices. We present experimental results for a range of applications spanning multiple domains and demonstrate highly efficient execution compared to manually-optimized counterparts in multiple distributed programming models.

关键词： Programming Hardware Parallel processing Computational modeling Sparks optimization Program processors

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