Most virtual machines employ just-in-time (JIT) compilers to achieve high-performance. Trace-based compilation and method-based compilation are two major compilation strategies in JIT compilers. In general, the former excels in compiling programs with more in-depth method calls and more dynamic branches, while the latter is suitable for a wide range of programs. Some previous studies have suggested that each strategy has its advantages and disadvantages, and there is no clear winner.
In this paper, we present a new approach, namely, the meta-hybrid JIT compilation strategy. It combines trace-based and method-based compilations to utilize the advantages of both strategies. Moreover, it is realized as a meta JIT compiler framework; thus, we can generate a VM with a hybrid JIT compiler that can apply different program parts by merely writing an interpreter with our framework.
We chose to extend a meta-tracing JIT compiler and supported the two compilations on it. As a prototype, we implemented a simple meta-tracing JIT compiler framework called BacCaml based on the MinCaml compiler by following RPython’s architecture.
We evaluated its performance by creating a small functional programming language with BacCaml and running microbenchmark programs. Furthermore, we performed a synthetic experiment to confirm that there are programs that run faster by hybrid compilation.
Execution times may be reduced by offloading parallel loop nests to a GPU. Auto-parallelizing compilers are common for static languages, often using a cost model to determine when the GPU execution speed will outweigh the offload overheads. Nowadays scientific software is increasingly written in dynamic languages and would benefit from compute accelerators. The ALPyNA framework analyses moderately complex Python loop nests and automatically JIT compiles code for heterogeneous CPU and GPU architectures.
We present the first analytical cost model for auto-parallelizing loop nests in a dynamic language on heterogeneous architectures. Predicting execution time in a language like Python is extremely challenging, since aspects like the element types, size of the iteration space, and amenability to parallelization can only be determined at runtime. Hence the cost model must be both staged, to combine compile and run-time information, and lightweight to minimize runtime overhead. GPU execution time prediction must account for factors like data transfer, block-structured execution, and starvation.
We show that a comparatively simple, staged analytical model can accurately determine during execution when it is profitable to offload a loop nest. We evaluate our model on three heterogeneous platforms across 360 experiments with 12 loop-intensive Python benchmark programs. The results show small misprediction intervals and a mean slowdown of just 13.6%, relative to the optimal (oracular) offload strategy.
Python is a popular, dynamic language for data science and scientific computing. To ensure efficiency, significant numerical libraries are implemented in static native languages. However, performance suffers when switching between native and non-native code, especially if data has to be converted between native arrays and Python data structures. As GPU accelerators are increasingly used, this problem becomes particularly acute. Data and control has to be repeatedly transferred between the accelerator and the host.
In this paper, we present DelayRepay, a delayed execution framework for numeric Python programs. It avoids excessive switching and data transfer by using lazy evaluation and kernel fusion. Using DelayRepay, operations on NumPy arrays are executed lazily, allowing multiple calls to accelerator kernels to be fused together dynamically. DelayRepay is available as a drop-in replacement for existing Python libraries. This approach enables significant performance improvement over the state-of-the-art and is invisible to the application programmer. We show that our approach provides a maximum 377× speedup over NumPy - a 409% increase over the state of the art.
Python 3 is a highly dynamic language, but it has introduced a syntax for expressing types with PEP484. This paper explores how developers use these type annotations, the type system semantics provided by type checking and inference tools, and the performance of these tools. We evaluate the types and tools on a corpus of public GitHub repositories. We review MyPy and PyType, two canonical static type checking and inference tools, and their distinct approaches to type analysis. We then address three research questions: (i) How often and in what ways do developers use Python 3 types? (ii) Which type errors do developers make? (iii) How do type errors from different tools compare?
Surprisingly, when developers use static types, the code rarely type-checks with either of the tools. MyPy and PyType exhibit false positives, due to their static nature, but also flag many useful errors in our corpus. Lastly, MyPy and PyType embody two distinct type systems, flagging different errors in many cases. Understanding the usage of Python types can help guide tool-builders and researchers. Understanding the performance of popular tools can help increase the adoption of static types and tools by practitioners, ultimately leading to more correct and more robust Python code.
Debugging applications that execute within a framework is not always easy: the call-stack offered to developers is often a mix-up of stack frames that belong to different frameworks, introducing an unnecessary noise that prevents developers from focusing on the debugging task. Moreover, relevant application code is not always available in the call-stack because it may have already returned, or is available in another thread. In such cases, manually gathering all relevant information from these different sources is not only cumbersome but also costly.
In this paper we introduce Sarto, a call-stack instrumentation layer that allows developers to tailor the stack to make debugging framework-aware. The goal is to improve the quality and amount information present in the call-stack to reduce debugging time without impacting the execution time. Sarto proposes a set of six stack operations that combined hide irrelevant information, introduce missing information, and relate dispersed debugging sources before this is fed to the debugger.
We validate Sarto by applying it to four application cases using inherently different frameworks: unit testing, web server, remote promises and big data processing. We showcase our experiences in using Sarto in the different frameworks, and perform some performance benchmarks to demonstrate that Sarto does not generate noticeable overhead when instrumenting a call-stack. We also show that our solution reduces by half the amount of data stored to debug similar exceptions happening in a parallel setup.
Pattern matching allows programs both to extract specific information from complex data types, as well as to branch on the structure of data and thus apply specialized actions to different forms of data. Originally designed for strongly typed functional languages with algebraic data types, pattern matching has since been adapted for object-oriented and even dynamic languages. This paper discusses how pattern matching can be included in the dynamically typed language Python in line with existing features that support extracting values from sequential data structures.
To efficiently execute dynamically typed languages, many language implementations have adopted a two-tier architecture. The first tier aims for low-latency startup times and collects dynamic profiles, such as the dynamic types of variables. The second tier provides high-throughput using an optimizing compiler that specializes code to the recorded type information. If the program behavior changes to the point that not previously seen types occur in specialized code, that specialized code becomes invalid, it is deoptimized, and control is transferred back to the first tier execution engine which will start specializing anew. However, if the program behavior becomes more specific, for instance, if a polymorphic variable becomes monomorphic, nothing changes. Once the program is running optimized code, there are no means to notice that an opportunity for optimization has been missed.
We propose to employ a sampling-based profiler to monitor native code without any instrumentation. The absence of instrumentation means that when the profiler is not active, no overhead is incurred. We present an implementation is in the context of the Ř just-in-time, optimizing compiler for the R language. Based on the sampled profiles, we are able to detect when the native code produced by Ř is specialized for stale type feedback and recompile it to more type-specific code. We show that sampling adds an overhead of less than 3
Many researchers have explored retrofitting static type systems to dynamic languages. This raises the question of how to add type annotations to code that was previously untyped. One obvious solution is type inference. However, in complex type systems, in particular those with structural types, type inference typically produces most general types that are large, hard to understand, and unnatural for programmers. To solve this problem, we introduce InferDL, a novel Ruby type inference system that infers sound and useful type annotations by incorporating heuristics that guess types. For example, we might heuristically guess that a parameter whose name ends in “count” is an integer. InferDL works by first running standard type inference and then applying heuristics to any positions for which standard type inference produces overly-general types. Heuristic guesses are added as constraints to the type inference problem to ensure they are consistent with the rest of the program and other heuristic guesses; inconsistent guesses are discarded. We formalized InferDL in a core type and constraint language. We implemented InferDL on top of RDL, an existing Ruby type checker. To evaluate InferDL, we applied it to four Ruby on Rails apps that had been previously type checked with RDL, and hence had type annotations. We found that, when using heuristics, InferDL inferred 22% more types that were as or more precise than the previous annotations, compared to standard type inference without heuristics. We also found one new type error. We further evaluated InferDL by applying it to six additional apps, finding five additional type errors. Thus, we believe InferDL represents a promising approach for inferring type annotations in dynamic languages.