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UDF to SQL Translation through Compositional Lazy Inductive Synthesis

PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL 2021年第OOPSLA期5卷 1–26页

作者： Zhang, Guoqiang Xu, Yuanchao Shen, Xipeng Dillig, Isil North Carolina State Univ Raleigh NC 27695 USA Univ Texas Austin Austin TX 78712 USA

Many data processing systems allow SQL queries that call user-defined functions (UDFs) written in conventional programming languages. While such SQL extensions provide convenience and flexibility to users, queries involving UDFs are not as efficient as their pure SQL counterparts that invoke SQL's highly-optimized built-in functions. Motivated by this problem, we propose a new technique for translating SQL queries with UDFs to pure SQL expressions. Unlike prior work in this space, our method is not based on syntactic rewrite rules and can handle a much more general class of UDFs. At a high-level, our method is based on counterexample-guided inductive synthesis (CEGIS) but employs a novel compositional strategy that decomposes the synthesis task into simpler sub-problems. However, because there is no universal decomposition strategy that works for all UDFs, we propose a novel lazy inductive synthesis approach that generates a sequence of decompositions that correspond to increasingly harder inductive synthesis problems. Because most realistic UDF-to-SQL translation tasks are amenable to a fine-grained decomposition strategy, our lazy inductive synthesis method scales significantly better than traditional CEGIS. We have implemented our proposed technique in a tool called CLIS for optimizing Spark SQL programs containing Scala UDFs. To evaluate CLIS, we manually study 100 randomly selected UDFs and find that 63 of them can be expressed in pure SQL. Our evaluation on these 63 UDFs shows that CLIS can automatically synthesize equivalent SQL expressions in 92% of the cases and that it can solve 2.4x more benchmarks compared to a baseline that does not use our compositional approach. We also show that CLIS yields an average speed-up of 3.5x for individual UDFs and 1.3x to 3.1x in terms of end-to-end application performance.

关键词： program synthesis source-to-source compiler query optimization

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A compiler for Sound Floating-Point Computations using Affine Arithmetic 22

A Compiler for Sound Floating-Point Computations using Affin...

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20th IEEE/ACM International Symposium on Code Generation and Optimization (CGO)

作者： Rivera, Joao Franchetti, Franz Puschel, Markus Swiss Fed Inst Technol Comp Sci Zurich Switzerland Carnegie Mellon Univ Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781665405843

Floating-point arithmetic is extensively used in scientific and engineering applications to approximate real arithmetic. Unfortunately, floating-point arithmetic is not a sound implementation of real arithmetic, i.e., it may produce different results, does not provide error guarantees, and the errors can become arbitrarily large. In this paper, we introduce SafeGen, a source-to-source compiler that rewrites a given C program using floating-point arithmetic to an efficient C program performing the same computation soundly, i.e., it returns an error bound that is guaranteed to contain the correct result of the program if it had been executed in real arithmetic. Equivalently, it gives a precision certificate on the number of correct bits in the result. SafeGen uses affine arithmetic (AA) that keeps accuracy high compared to interval arithmetic by preserving linear correlations between variables. To mitigate its high cost, SafeGen combines a novel form of static analysis to identify these correlations with a flexible policy-based approach for their selection. SafeGen supports SIMD intrinsics in the input and can output SIMD-optimized code. Our results show that SafeGen-generated code is 30-70 times faster than manually rewritten code using AA libraries. Equivalently, SafeGen can offer many more bits of certified accuracy within a reduced time budget.

关键词： floating-point arithmetic source-to-source compiler affine arithmetic guaranteed computations

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The Framework and Compilation Techniques for Directive-based GPU Cluster Programming

The Framework and Compilation Techniques for Directive-based...

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11th International Joint Conference on Computer Science and Software Engineering (JCSSE)

作者： Makpaisit, Pisit Uthayopas, Putchong Kasetsart Univ Fac Engn Dept Comp Engn HPCNC Bangkok Thailand

ISBN: (纸本)9781479958221

GPU cluster is an important architecture being used for large scientific and engineering applications. However, manually developed GPU cluster application is still a very difficult task. To alleviate this problem, we adopt the OpenACC standard for directive-based approach and proposed some extension to support GPU cluster programming. The extensions are constructs and clauses used to define the memory distribution and dependency of tasks on cluster nodes. We propose framework and technique used to implement a source-to-source compiler to support the proposed constructs and clauses. The experiment conducted on the source code translation tool developed in this work shows that the speedup close to hand code can be achieved on commonly used scientific application with much less programming effort.

关键词： GPGPU Cluster Parallel Programming source-to-source compiler OpenACC

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C and OpenCL Generation from MATLAB 15

C and OpenCL Generation from MATLAB

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30th ACM Symposium on Applied Computing (SAC)

作者： Bispo, Joao Reis, Luis Cardoso, Joao M. P. Univ Porto Fac Engn FEUP Oporto Portugal Univ Porto INESC TEC FEUP Oporto Portugal

ISBN: (纸本)9781450331968

In many engineering and science areas, models are developed and validated using high-level programing languages and environments as is the case with MATLAB. In order to target the multicore heterogeneous architectures being used on embedded systems to provide high performance computing with acceptable energy/power envelops, developers manually migrate critical code sections to lower-level languages such as C and OpenCL, a time consuming and error prone process. Thus, automatic source-to-source approaches are highly desirable. We present an approach to compile MATLAB and output equivalent C/OpenCL code to target architectures, such as GPU based hardware accelerators. We evaluate our approach on an existing MATLAB compiler framework named MATISSE. The OpenCL generation relies on the manual insertion of directives to guide the compilation and is also capable of generating C wrapper code to interface and synchronize with the OpenCL code. We evaluated the compiler with a number of benchmarks from different domains and the results are very encouraging.

关键词： MATLAB source-to-source compiler C programming language embedded systems GPUs OpenCL

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Leveraging HW Approximation for Exploiting Performance-Energy Trade-offs Within the Edge-Cloud Computing Continuum

Leveraging HW Approximation for Exploiting Performance-Energ...

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35th International ISC High Performance Conference

作者： Kokkinis, Argyris Ferikoglou, Aggelos Danopoulos, Dimitrios Masouros, Dimosthenis Siozios, Kostas Aristotle Univ Thessaloniki Thessaloniki Greece

ISBN: (纸本)9783030905392;9783030905385

Today, the need for real-time analytics and faster decision making mechanisms has led to the adoption of hardware accelerators, such as GPUs and FPGAs, within the edge-cloud computing continuum. Moreover, the need for energy-, yet performance-efficient solutions both in the edge and cloud has led to the rise of approximate computing as a promising paradigm, where "acceptable errors" are introduced to error-tolerant applications, thus, providing significant power-saving gains. In this work, we leverage approximate computing for exploiting performance-energy trade-offs of FPGA accelerated kernels with faster design time though an extended source-to-source HLS compiler based on Xilinx Vitis framework. We introduce a novel programming interface that operates at a high level of abstraction, thus, enabling automatic optimizations to the existing HLS design flow supporting both embedded and cloud devices through a common API. We evaluate our approach over three different application from DSP and machine learning domains and show that a decrease of 27% and 28% in power consumption, 61% and 69% in DSP utilization and 7% in clock period is achieved for Alveo U200 and ZCU104 FPGA platforms, on average.

关键词： Edge & cloud computing FPGA HW approximation High Level Synthesis source-to-source compiler

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A Flexible-Granularity Task Graph Representation and Its Generation from C Applications (WIP) 25

A Flexible-Granularity Task Graph Representation and Its Gen...

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25th ACM SIGPLAN/SIGBED International Conference on Languages, compilers, and Tools for Embedded Systems (LCTES)

作者： Santos, Tiago Bispo, Joao Cardoso, Joao M. P. Univ Porto FEUP INESC TEC Porto Portugal

ISBN: (纸本)9798400706165

Modern hardware accelerators, such as FPGAs, allow offloading large regions of C/C++ code in order to improve the execution time and/or the energy consumption of software applications. An outstanding challenge with this approach, however, is solving the Hardware/Software (Hw/Sw) partitioning problem. Given the increasing complexity of both the accelerators and the potential code regions, one needs to adopt a holistic approach when selecting an offloading region by exploring the interplay between communication costs, data usage patterns, and target-specific optimizations. To this end, we propose representing a C application as an extended task graph (ETG) with flexible granularity, which can be manipulated through the merging and splitting of tasks. This approach involves generating a task graph overlay on the program's Abstract Syntax Tree (AST) that maps tasks to functions and the flexible granularity operations onto inlining/outlining operations. This maintains the integrity and readability of the original source code, which is paramount for targeting different accelerators and enabling code optimizations, while allowing the offloading of code regions of arbitrary complexity based on the data patterns of their tasks. To evaluate the ETG representation and its compiler, we use the latter to generate ETGs for the programs in Rosetta and MachSuite benchmark suites, and extract several metrics regarding data communication, task-level parallelism, and dataflow patterns between pairs of tasks. These metrics provide important information that can be used by Hw/Sw partitioning methods.

关键词： Task Graph source-to-source compiler Hardware/Software Partitioning Hardware Accelerators FPGA

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An Interval compiler for Sound Floating-Point Computations 21

An Interval Compiler for Sound Floating-Point Computations

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19th IEEE/ACM International Symposium on Code Generation and Optimization (CGO)

作者： Rivera, Joao Franchetti, Franz Puschel, Markus Swiss Fed Inst Technol Comp Sci Zurich Switzerland Carnegie Mellon Univ Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781728186139

Floating-point arithmetic is widely used by software developers but is unsound, i.e., there is no guarantee on the accuracy obtained, which can be imperative in safety-critical applications. We present IGen, a source-to-source compiler that translates a given C function using floating-point into an equivalent sound C function that uses interval arithmetic. IGen supports Intel SIMD intrinsics in the input function using a specially designed code generator and can produce SIMD-optimized output. To mitigate a possible loss of accuracy due to the increase of interval sizes, IGen can compile to double-double precision, again SIMD-optimized. Finally, IGen implements an accuracy optimization for the common reduction pattern. We benchmark our compiler on high-performance code in the domain of linear algebra and signal processing. The results show that the generated code delivers sound double precision results at high performance. In particular, we observe speed-ups of up to 9.8 when compared to commonly used interval libraries using double precision. When compiling to double-double, our compiler delivers intervals that keep error accumulation small enough to compute results with at most one bit of error in double precision, i.e., certified double precision results.

关键词： floating-point arithmetic source-to-source compiler interval arithmetic certified accuracy

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Performance Estimation of High-Level Dataflow Program on Heterogeneous Platforms 14

Performance Estimation of High-Level Dataflow Program on Het...

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14th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip (MCSoC)

作者： Bloch, Aurelien Brunet, Simone Casale Mattavelli, Marco Ecole Polytech Fed Lausanne SCI MM STI Lausanne Switzerland

ISBN: (纸本)9781665438605

The performance of programs written in languages following the dataflow model of computation (MoC) largely depends on the configuration (partitioning, mapping, scheduling, buffer dimensioning) chosen during the synthesis stages. Furthermore, this programming paradigm is particularly well suited for heterogeneous parallel systems because it is inherently free of memory contention and exposes parallel opportunities. Both of these statements show the necessity for a way to easily and automatically evaluate and find good design configurations. The paper describes the methodology required for clock-accurate profiling of high-level dataflow programs written in RVL-CAL when synthesized on heterogeneous CPU/GPU co-processing platforms. It also extends to the heterogeneous paradigm an existing methodology for qualitatively estimating the performance of such programs as a function of the provided configuration. This, without the need to synthesize and profile every single configuration on the actual hardware platform. This approach is validated using two application programs and several configurations.

关键词： dynamic dataflow programs RVC-CAL profiling performance estimation parallel computing source-to-source compiler GPU programming heterogeneous systems

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MiX10: Compiling MATLAB to X10 for High Performance 14

MiX10: Compiling MATLAB to X10 for High Performance

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2014 ACM International Conference on Object-Oriented-Programming-Systems-Languages-and-Applications (OOPSLA 14)

作者： Kumar, Vineet Hendren, Laurie McGill Univ Montreal PQ H3A 2T5 Canada

ISBN: (纸本)9781450325851

MATLAB is a popular dynamic array-based language commonly used by students, scientists and engineers who appreciate the interactive development style, the rich set of array operators, the extensive builtin library, and the fact that they do not have to declare static types. Even though these users like to program in MATLAB, their computations are often very compute-intensive and are better suited for emerging high performance computing systems. This paper reports on MIX 10, a source-to-source compiler that automatically translates MATLAB programs to X10, a language designed for "Performance and Productivity at Scale";thus, helping scientific programmers make better use of high performance computing systems. There is a large semantic gap between the array-based dynamically-typed nature of MATLAB and the object-oriented, statically-typed, and high-level array abstractions of X10. This paper addresses the major challenges that must be overcome to produce sequential X10 code that is competitive with state-of-the-art static compilers for MATLAB which target more conventional imperative languages such as C and Fortran. Given that efficient basis, the paper then provides a translation for the MATLAB par for construct that leverages the powerful concurrency constructs in X10. The MIX 10 compiler has been implemented using the McLab compiler tools, is open source, and is available both for compiler researchers and end-user MATLAB programmers. We have used the implementation to perform many empirical measurements on a set of 17 MATLAB benchmarks. We show that our best MIX 10-generated code is significantly faster than the de facto Mathworks' MATLAB system, and that our results are competitive with state-of-the-art static compilers that target C and Fortran. We also show the importance of finding the correct approach to representing arrays in X10, and the necessity of an IntegerOkay analysis that determines which double variables can be safely represented as integers. Finally,

关键词： Experimentation Languages MATLAB X10 source-to-source compiler

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A JavaScript Transpiler for Escaping from Complicated Usage of Cloud Services and APIs 25

A JavaScript Transpiler for Escaping from Complicated Usage ...

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25th Asia-Pacific Software Engineering Conference (APSEC)

作者： Kimura, Kosaku Sekiguchi, Atsuji Choudhary, Shridhar Uehara, Tadahiro Fujitsu Labs Ltd Kawasaki Kanagawa Japan

ISBN: (纸本)9781728119700

We propose Escapin, a JavaScript transpiler for developing application programs that consume APIs and are deployed on cloud services in order to obtain new business concepts by trial-and-error iterations. Escapin tackles two difficulties in consuming API and using cloud services;1) understanding the correct usage of cloud services and APIs requires a lot of time, and 2) programs using cloud service SDKs and HTTP clients are complicated. Escapin introduces two new features to the ECMAScript 2018 specification for simplifying application programs: importing APIs as objects and exporting objects and functions as cloud service resources. Escapin transpiles a JavaScript program independent from any platform into a JavaScript program containing concrete usages of APIs and cloud services. Escapin encourages engineers to write a callback-agnostic program, which represents asynchronous processing as a completely synchronized procedure without any asynchronization feature such as callback, Promise, async/await, etc. Escapin also accepts programs having nesting callbacks. Escapin destructures nesting callbacks in the programs to obtain a callback-agnostic program. Our experimental results show that in the task of developing an application that consumes Uber API and is deployed on Amazon Web Services, Escapin statistically significantly reduces development time and the metrics regarding the simplicity of source code with huge effects: lines of code, cyclomatic complexity, and cognitive complexity.

关键词： Transpiler source-to-source compiler Meta-programming Cloud Computing Web APIs

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