Static Compiler Research Articles

Modularized and scalable compilation for double quantum dot quantum computing

Any quantum program on a realistic quantum device must be compiled into an executable form while taking into account the underlying hardware constraints. Stringent restrictions on architecture and control imposed by physical platforms make this very challenging. In this paper, based on the quantum variational algorithm, we propose a novel scheme to train an Ansatz circuit and realize high-fidelity compilation of a set of universal quantum gates for singlet-triplet qubits in semiconductor double quantum dots, a fairly heavily constrained system. Furthermore, we propose a scalable architecture for a modular implementation of quantum programs in this constrained systems and validate its performance with two representative demonstrations, the Grover’s algorithm for the database searching (static compilation) and a variant of variational quantum eigensolver for the Max-Cut optimization (dynamic compilation). Our methods are potentially applicable to a wide range of physical devices. This work constitutes an important stepping-stone for exploiting the potential for advanced and complicated quantum algorithms on near-term devices.

Formally Verified Native Code Generation in an Effectful JIT: Turning the CompCert Backend into a Formally Verified JIT Compiler

Modern Just-in-Time compilers (or JITs) typically interleave several mechanisms to execute a program. For faster startup times and to observe the initial behavior of an execution, interpretation can be initially used. But after a while, JITs dynamically produce native code for parts of the program they execute often. Although some time is spent compiling dynamically, this mechanism makes for much faster times for the remaining of the program execution. Such compilers are complex pieces of software with various components, and greatly rely on a precise interplay between the different languages being executed, including on-stack-replacement. Traditional static compilers like CompCert have been mechanized in proof assistants, but JITs have been scarcely formalized so far, partly due to their impure nature and their numerous components. This work presents a model JIT with dynamic generation of native code, implemented and formally verified in Coq. Although some parts of a JIT cannot be written in Coq, we propose a proof methodology to delimit, specify and reason on the impure effects of a JIT. We argue that the daunting task of formally verifying a complete JIT should draw on existing proofs of native code generation. To this end, our work successfully reuses CompCert and its correctness proofs during dynamic compilation. Finally, our prototype can be extracted and executed.

Compilation Forking: A Fast and Flexible Way of Generating Data for Compiler-Internal Machine Learning Tasks

Compiler optimization decisions are often based on hand-crafted heuristics centered around a few established benchmark suites. Alternatively, they can be learned from feature and performance data produced during compilation. However, data-driven compiler optimizations based on machine learning models require large sets of quality data for training in order to match or even outperform existing human-crafted heuristics. In static compilation setups, related work has addressed this problem with iterative compilation. However, a dynamic compiler may produce different data depending on dynamically-chosen compilation strategies, which aggravates the generation of comparable data. We propose compilation forking, a technique for generating consistent feature and performance data from arbitrary, dynamically-compiled programs. Different versions of program parts with the same profiling and compilation history are executed within single program runs to minimize noise stemming from dynamic compilation and the runtime environment. Our approach facilitates large-scale performance evaluations of compiler optimization decisions. Additionally, compilation forking supports creating domain-specific compilation strategies based on machine learning by providing the data for model training. We implemented compilation forking in the GraalVM compiler in a programming-language-agnostic way. To assess the quality of the generated data, we trained several machine learning models to replace compiler heuristics for loop-related optimizations. The trained models perform equally well to the highly-tuned compiler heuristics when comparing the geometric means of benchmark suite performances. Larger impacts on few single benchmarks range from speedups of 20% to slowdowns of 17%. The presented approach can be implemented in any dynamic compiler. We believe that it can help to analyze compilation decisions and leverage the use of machine learning into dynamic compilation.

Of JavaScript AOT compilation performance

The fastest JavaScript production implementations use just-in-time (JIT) compilation and the vast majority of academic publications about implementations of dynamic languages published during the last two decades focus on JIT compilation. This does not imply that static compilers (AoT) cannot be competitive; as comparatively little effort has been spent creating fast AoT JavaScript compilers, a scientific comparison is lacking. This paper presents the design and implementation of an AoT JavaScript compiler, focusing on a performance analysis. The paper reports on two experiments, one based on standard JavaScript benchmark suites and one based on new benchmarks chosen for their diversity of styles, authors, sizes, provenance, and coverage of the language. The first experiment shows an advantage to JIT compilers, which is expected after the decades of effort that these compilers have paid to these very tests. The second shows more balanced results, as the AoT compiler generates programs that reach competitive speeds and that consume significantly less memory. The paper presents and evaluates techniques that we have either invented or adapted from other systems, to improve AoT JavaScript compilation.

Loop Selection for Multilevel Nested Loops Using a Genetic Algorithm

Loop selection for multilevel nested loops is a very difficult problem, for which solutions through the underlying hardware-based loop selection techniques and the traditional software-based static compilation techniques are ineffective. A genetic algorithm- (GA-) based method is proposed in this study to solve this problem. First, the formal specification and mathematical model of the loop selection problem are presented; then, the overall framework for the GA to solve the problem is designed based on the mathematical model; finally, we provide the chromosome representation method and fitness function calculation method, the initial population generation algorithm and chromosome improvement methods, the specific implementation methods of genetic operators (crossover, mutation, and selection), the offspring population generation method, and the GA stopping criterion during the GA operation process. Experimental tests with the SPEC2006 and NPB3.3.1 standard test sets were performed on the Sunway TaihuLight supercomputer. The test results indicated that the proposed method can achieve a speedup improvement that is superior to that by the current mainstream methods, which confirm the effectiveness of the proposed method. Solving the loop selection problem of multilevel nested loops is of great practical significance for exploiting the parallelism of general scientific computing programs and for giving full play to the performance of multicore processors.

Pattern-Based Dynamic Compilation System for CGRAs With Online Configuration Transformation

Prevailing data-intensive applications, such as artificial intelligence and internet of things, demand considerable compute capability. Coarse-grained reconfigurable architectures (CGRAs) can meet this demand via providing abundant compute resources. However, compilation has become an essential problem because the increasing resources need to be orchestrated efficiently. Static compilation is insufficient due to conservative resource allocation and exponentially increasing time cost while state-of-the-art dynamic compilation still performs poorly in both generality and efficiency. This article proposes a dynamic compilation system for CGRAs through online pattern-based configuration transformation, which enables virtualization to improve resource utilization and flexibility. It utilizes statically-generated patterns to straightforwardly determine dynamic placement of registers and operations so that the transformation algorithm has a low complexity. Domain-specific features are extracted by a k-means clustering algorithm to help improve the quality of patterns. The experimental results show that statically compiled applications can be transformed onto arbitrary resources at runtime, reserving 73.5 (22.8-163.3 percent) of the original performance/resource on average, 9.1 (0-52.9 percent) better than the state-of-theart non-general methods.

Hidden inheritance: an inline caching design for TypeScript performance

TypeScript is a dynamically typed language widely used to develop large-scale applications nowadays. These applications are usually designed with complex class or interface hierarchies and have highly polymorphic behaviors. These object-oriented (OO) features will lead to inefficient inline caches (ICs) or trigger deoptimizations, which impact the performance of TypeScript applications. To address this problem, we introduce an inline caching design called hidden inheritance (HI). The basic idea of HI is to cache the static information of class or interface hierarchies into hidden classes, which are leveraged to generate efficient inline caches for improving the performance of OO-style TypeScript programs. The HI design is implemented in a TypeScript engine STSC (Static TypeScript Compiler) including a static compiler and a runtime system. STSC statically generates hidden classes and enhanced inline caches, which are applied to generate specialized machine code via ahead-of-time compilation (AOTC) or just-in-time compilation (JITC). To evaluate the efficiency of this technique, we implement STSC on a state-of-the-art JavaScript virtual machine V8 and demonstrate its performance improvements on industrial benchmarks and applications.

Kinds are calling conventions

A language supporting polymorphism is a boon to programmers: they can express complex ideas once and reuse functions in a variety of situations. However, polymorphism is pain for compilers tasked with producing efficient code that manipulates concrete values. This paper presents a new intermediate language that allows for efficient static compilation, while still supporting flexible polymorphism. Specifically, it permits polymorphism over not only the types of values, but also the representation of values, the arity of primitive machine functions, and the evaluation order of arguments---all three of which are useful in practice. The key insight is to encode information about a value's calling convention in the kind of its type, rather than in the type itself.

Deep neural networks compiler for a trace-based accelerator

Convolutional Neural Networks (CNNs) are the algorithm of choice for image processing applications. CNNs are a highly parallel workload that leads to the emergence of custom hardware accelerators. Deep Learning (DL) models specialized in different tasks require programmable custom hardware and a compiler/mapper to efficiently translate different CNNs into an efficient dataflow in the accelerator. The goal of this paper is to present a compiler for running CNNs on programmable custom hardware accelerators with a domain-specific ISA that targets CNNs. In this work, the compiler was evaluated and tested on a hardware accelerator that was presented in [18]. The compiler uses model definition files created from popular frameworks to generate custom instructions. The model goes through static compilation and different levels of hardware aware optimizations that improve performance and data reuse of the generated program. The software also exposes an interface to run on various FPGA platforms, providing an end-to-end solution. Various CNN models were benchmarked on different systems while scaling the number of processing units.

PYE

Languages like Java and C# follow a two-step process of compilation: static compilation and just-in-time (JIT) compilation. As the time spent in JIT compilation gets added to the execution-time of the application, JIT compilers typically sacrifice the precision of program analyses for efficiency. The alternative of performing the analysis for the whole program statically ignores the analysis of libraries (available only at runtime), and thereby generates imprecise results. To address these issues, in this article, we propose a two-step (static+JIT) analysis framework called precise-yet-efficient (PYE) that helps generate precise analysis-results at runtime at a very low cost. PYE achieves the twin objectives of precision and performance during JIT compilation by using a two-pronged approach: (i) It performs expensive analyses during static compilation, while accounting for the unavailability of the runtime libraries by generating partial results, in terms of conditional values , for the input application. (ii) During JIT compilation, PYE resolves the conditions associated with these values, using the pre-computed conditional values for the libraries, to generate the final results. We have implemented the static and the runtime components of PYE in the Soot optimization framework and the OpenJDK HotSpot Server Compiler (C2), respectively. We demonstrate the usability of PYE by instantiating it to perform two context-, flow-, and field-sensitive heap-based analyses: (i) points-to analysis for null-dereference-check elimination; and (ii) escape analysis for synchronization elimination. We evaluate these instantiations against their corresponding state-of-the-art implementations in C2 over a wide range of benchmarks. The extensive evaluation results show that our strategy works quite well and fulfills both the promises it makes: enhanced precision while maintaining efficiency during JIT compilation.

Automated Software Protection for the Masses Against Side-Channel Attacks

We present an approach and a tool to answer the need for effective, generic, and easily applicable protections against side-channel attacks. The protection mechanism is based on code polymorphism, so that the observable behaviour of the protected component is variable and unpredictable to the attacker. Our approach combines lightweight specialized runtime code generation with the optimization capabilities of static compilation. It is extensively configurable. Experimental results show that programs secured by our approach present strong security levels and meet the performance requirements of constrained systems.

Static Compiler Analyses for Application-specific Optimization of Task-Parallel Runtime Systems

Achieving high performance in task-parallel runtime systems, especially with high degrees of parallelism and fine-grained tasks, requires tuning a large variety of behavioral parameters according to program characteristics. In the current state of the art, this tuning is generally performed in one of two ways: either by a group of experts who derive a single setup which achieves good – but not optimal – performance across a wide variety of use cases, or by monitoring a system’s behavior at runtime and responding to it. The former approach invariably fails to achieve optimal performance for programs with highly distinct execution patterns, while the latter induces overhead and cannot affect parameters which need to be set at compile time. In order to mitigate these drawbacks, we propose a set of novel static compiler analyses specifically designed to determine program features which affect the optimal settings for a task-parallel execution environment. These features include the parallel structure of task spawning, the granularity of individual tasks, the memory size of the closure required for task parameters, and an estimate of the stack size required per task. Based on the result of these analyses, various runtime system parameters are then tuned at compile time. We have implemented this approach in the Insieme compiler and runtime system, and evaluated its effectiveness on a set of 12 task parallel benchmarks running with 1 to 64 hardware threads. Across this entire space of use cases, our implementation achieves a geometric mean performance improvement of 39%. To illustrate the impact of our optimizations, we also provide a comparison to current state-of-the art task-parallel runtime systems, including OpenMP, Cilk, HPX, and Intel TBB.

Ahead-of-time compilation of JavaScript programs

Modern virtual machines for JavaScript use just-in-time (JIT) compilation to produce binary code. JIT compilers cannot perform complex optimizations. In contrast, static compilation has unlimited capabilities for complex optimizing transformations, but it cannot be efficiently applied to dynamic languages, such as JavaScript. In this paper, a general approach to the ahead-of-time compilation of programs in dynamic languages is proposed, and this approach is used for improving two virtual machines JavaScript- Core and V8. In the implementation of the improved JavaScriptCore engine with ahead-of-time compilation, the specifics of using JavaScript programs as a part of locally stored applications for the ARM platform were taken into account. In the V8 engine for the x86-64 platform, the ahead-of-time compilation is implemented by caching an optimized internal representation in a separate file.

Type inference for static compilation of JavaScript

We present a type system and inference algorithm for a rich subset of JavaScript equipped with objects, structural subtyping, prototype inheritance, and first-class methods. The type system supports abstract and recursive objects, and is expressive enough to accommodate several standard benchmarks with only minor workarounds. The invariants enforced by the types enable an ahead-of-time compiler to carry out optimizations typically beyond the reach of static compilers for dynamic languages. Unlike previous inference techniques for prototype inheritance, our algorithm uses a combination of lower and upper bound propagation to infer types and discover type errors in all code, including uninvoked functions. The inference is expressed in a simple constraint language, designed to leverage off-the-shelf fixed point solvers. We prove soundness for both the type system and inference algorithm. An experimental evaluation showed that the inference is powerful, handling the aforementioned benchmarks with no manual type annotation, and that the inferred types enable effective static compilation.

CUDAMPF: a multi-tiered parallel framework for accelerating protein sequence search in HMMER on CUDA-enabled GPU.

BackgroundHMMER software suite is widely used for analysis of homologous protein and nucleotide sequences with high sensitivity. The latest version of hmmsearch in HMMER 3.x, utilizes heuristic-pipeline which consists of MSV/SSV (Multiple/Single ungapped Segment Viterbi) stage, P7Viterbi stage and the Forward scoring stage to accelerate homology detection. Since the latest version is highly optimized for performance on modern multi-core CPUs with SSE capabilities, only a few acceleration attempts report speedup. However, the most compute intensive tasks within the pipeline (viz., MSV/SSV and P7Viterbi stages) still stand to benefit from the computational capabilities of massively parallel processors.ResultsA Multi-Tiered Parallel Framework (CUDAMPF) implemented on CUDA-enabled GPUs presented here, offers a finer-grained parallelism for MSV/SSV and Viterbi algorithms. We couple SIMT (Single Instruction Multiple Threads) mechanism with SIMD (Single Instructions Multiple Data) video instructions with warp-synchronism to achieve high-throughput processing and eliminate thread idling. We also propose a hardware-aware optimal allocation scheme of scarce resources like on-chip memory and caches in order to boost performance and scalability of CUDAMPF. In addition, runtime compilation via NVRTC available with CUDA 7.0 is incorporated into the presented framework that not only helps unroll innermost loop to yield upto 2 to 3-fold speedup than static compilation but also enables dynamic loading and switching of kernels depending on the query model size, in order to achieve optimal performance.ConclusionsCUDAMPF is designed as a hardware-aware parallel framework for accelerating computational hotspots within the hmmsearch pipeline as well as other sequence alignment applications. It achieves significant speedup by exploiting hierarchical parallelism on single GPU and takes full advantage of limited resources based on their own performance features. In addition to exceeding performance of other acceleration attempts, comprehensive evaluations against high-end CPUs (Intel i5, i7 and Xeon) shows that CUDAMPF yields upto 440 GCUPS for SSV, 277 GCUPS for MSV and 14.3 GCUPS for P7Viterbi all with 100 % accuracy, which translates to a maximum speedup of 37.5, 23.1 and 11.6-fold for MSV, SSV and P7Viterbi respectively. The source code is available at https://github.com/Super-Hippo/CUDAMPF.

JavaScript Parallelizing Compiler for Exploiting Parallelism from Data-Parallel HTML5 Applications

With the advent of the HTML5 standard, JavaScript is increasingly processing computationally intensive, data-parallel workloads. Thus, the enhancement of JavaScript performance has been emphasized because the performance gap between JavaScript and native applications is still substantial. Despite this urgency, conventional JavaScript compilers do not exploit much of parallelism even from data-parallel JavaScript applications, despite contemporary mobile devices being equipped with expensive parallel hardware platforms, such as multicore processors and GPGPUs. In this article, we propose an automatically parallelizing JavaScript compiler that targets emerging, data-parallel HTML5 applications by leveraging the mature affine loop analysis of conventional static compilers. We identify that the most critical issues when parallelizing JavaScript with a conventional static analysis are ensuring correct parallelization, minimizing compilation overhead, and conducting low-cost recovery when there is a speculation failure during parallel execution. We propose a mechanism for safely handling the failure at a low cost, based on compiler techniques and the property of idempotence. Our experiment shows that the proposed JavaScript parallelizing compiler detects most affine parallel loops. Also, we achieved a maximum speedup of 3.22 times on a quad-core system, while incurring negligible compilation and recovery overheads with various sets of data-parallel HTML5 applications.

An Abstract Interpretation-Based Model of Tracing Just-in-Time Compilation

Tracing just-in-time compilation is a popular compilation technique for the efficient implementation of dynamic languages, which is commonly used for JavaScript, Python, and PHP. It relies on two key ideas. First, it monitors program execution in order to detect so-called hot paths, that is, the most frequently executed program paths. Then, hot paths are optimized by exploiting some information on program stores that is available and therefore gathered at runtime. The result is a residual program where the optimized hot paths are guarded by sufficient conditions ensuring some form of equivalence with the original program. The residual program is persistently mutated during its execution, for example, to add new optimized hot paths or to merge existing paths. Tracing compilation is thus fundamentally different from traditional static compilation. Nevertheless, despite the practical success of tracing compilation, very little is known about its theoretical foundations. We provide a formal model of tracing compilation of programs using abstract interpretation. The monitoring phase (viz., hot path detection) corresponds to an abstraction of the trace semantics of the program that captures the most frequent occurrences of sequences of program points together with an abstraction of their corresponding stores, for example, a type environment. The optimization phase (viz., residual program generation) corresponds to a transform of the original program that preserves its trace semantics up to a given observation as modeled by some abstraction. We provide a generic framework to express dynamic optimizations along hot paths and to prove them correct. We instantiate it to prove the correctness of dynamic type specialization and constant variable folding. We show that our framework is more general than the model of tracing compilation introduced by Guo and Palsberg [2011], which is based on operational bisimulations. In our model, we can naturally express hot path reentrance and common optimizations like dead-store elimination, which are either excluded or unsound in Guo and Palsberg’s framework.

Assisting Static Compiler Vectorization with a Speculative Dynamic Vectorizer in an HW/SW Codesigned Environment

Compiler-based static vectorization is used widely to extract data-level parallelism from computation-intensive applications. Static vectorization is very effective in vectorizing traditional array-based applications. However, compilers’ inability to do accurate interprocedural pointer disambiguation and interprocedural array dependence analysis severely limits vectorization opportunities. HW/SW codesigned processors provide an excellent opportunity to optimize the applications at runtime. The availability of dynamic application behavior at runtime helps in capturing vectorization opportunities generally missed by the compilers. This article proposes to complement the static vectorization with a speculative dynamic vectorizer in an HW/SW codesigned processor. We present a speculative dynamic vectorization algorithm that speculatively reorders ambiguous memory references to uncover vectorization opportunities. The speculative reordering of memory instructions avoids the need for accurate interprocedural pointer disambiguation and interprocedural array dependence analysis. The hardware checks for any memory dependence violation due to speculative vectorization and takes corrective action in case of violation. Our experiments show that the combined (static + dynamic) vectorization approach provides a 2× performance benefit compared to the static GCC vectorization alone, for SPECFP2006. Furthermore, the speculative dynamic vectorizer is able to vectorize 48% of the loops that ICC failed to vectorize due to conservative dependence analysis in the TSVC benchmark suite. Moreover, the dynamic vectorization scheme is as effective in vectorization of pointer-based applications as for the array-based ones, whereas compilers lose significant vectorization opportunities in pointer-based applications. Furthermore, we show that speculation is not only a luxury but also a necessity for runtime vectorization.

Generating safe boundary APIs between typed EDSLs and their environments

Embedded domain specific languages (EDSLs) are used to represent special-purpose code in a general-purpose language and they are used for applications like vector calculations and run-time code generation. Often, code in an EDSL is compiled to a target (e.g. GPU languages, JVM bytecode, assembly, JavaScript) and needs to interface with other code that is available at that level but uses other data representations or calling conventions. We present an approach for safely making available such APIs in a typed EDSL, guaranteeing correct conversions between data representations and the respect for calling conventions. When the code being interfaced with is the result of static compilation of host language code, we propose a way to auto-generate the needed boilerplate using meta-programming. We instantiate our technique with JavaScript as the target language, JS-Scala as the EDSL, Scala.js as the static compiler and Scala macros to generate the boilerplate, but our design is more generally applicable. We provide evidence of usefulness of our approach through a prototype implementation that we have applied in a non-trivial code base.

Pythran: enabling static optimization of scientific Python programs

Pythran is an open source static compiler that turns modules written in a subset of Python language into native ones. Assuming that scientific modules do not rely much on the dynamic features of the language, it trades them for powerful, possibly inter-procedural, optimizations. These optimizations include detection of pure functions, temporary allocation removal, constant folding, Numpy ufunc fusion and parallelization, explicit thread-level parallelism through OpenMP annotations, false variable polymorphism pruning, and automatic vector instruction generation such as AVX or SSE. In addition to these compilation steps, Pythran provides a C++ runtime library that leverages the C++ STL to provide generic containers, and the Numeric Template Toolbox for Numpy support. It takes advantage of modern C++11 features such as variadic templates, type inference, move semantics and perfect forwarding, as well as classical idioms such as expression templates. Unlike the Cython approach, Pythran input code remains compatible with the Python interpreter. Output code is generally as efficient as the annotated Cython equivalent, if not more, but without the backward compatibility loss.