arXiv subjects

Walter Binder

Publications and source records attributed to Walter Binder.

6 recordsLinked to original sources

arXiv2026

JEDI: Java Evaluation of Declarative and Imperative Queries

The Java Stream API aims at increasing developer productivity thanks to an easy-to-read declarative syntax to express computations. It also simplifies parallel computing, providing a high-level abstraction on top of common parallelization aspects. Unfortunately, there is a lack of benchmarks specifically targeting stream-based applications. Such a lack of benchmarks makes it difficult for researchers and developers of the Java class library to optimize the Stream API. Moreover, in the absence of dedicated benchmarks, it is difficult to analyze the performance of streams to suggest developers how to write efficient code using the API. In this work we present JEDI, a benchmark suite that targets the Stream API. JEDI is automatically generated by converting SQL benchmarks into Java benchmarks. Our code generator supports targets different implementations (both stream-based and imperative) for the same query. The ultimate goal of our benchmark suite -- and the main contribution of this work -- is to analyze the performance of the different implementations to spot inefficient code structures and better alternatives, suggesting best practices to Java developers. Among the multiple implementations we generate, we focus on different parallelization strategies and explain the most efficient parallelization strategies based on characteristics of the processed data. Finally, the code generation producing imperative code defines of a baseline that can guide researchers and Java implementers to optimize the Stream API.

cs.PL↗

arXiv2026

Misleading Microbenchmarks on the Java Virtual Machines

Developers often use microbenchmarks to choose the most performant implementation of a method or a class. On the Java Virtual Machine (JVM), this is commonly done using the Java Microbenchmark Harness (JMH) which addresses common pitfalls of measuring code performance on the JVM. However, even using JMH guidelines cannot overcome the fundamental issue of context. Microbenchmarks inherently execute code in isolation, without interference from other application code competing for CPU resources, such as cache or branch-predictor capacity. On managed runtimes with tiered dynamic compilation, such as the JVM, the speculative, profile-driven nature of compilation decisions means that code performance is highly dependent on profiles collected during early execution. Because profiles usually include also branch probabilities and receiver types (besides code hotness metrics), a badly designed microbenchmark may cause the JVM to collect an unrealistic profile, resulting in aggressive, yet misleading, optimizations, that would not occur in a real application. In this paper, we demonstrate how using microbenchmarks under conditions that induce the JVM to collect unrealistic profiles yields misleading results despite following existing guidelines. We also extend these guidelines by suggesting actions to make the microbenchmark results more representative.

cs.PL↗

arXiv2026

MapReplay: Trace-Driven Benchmark Generation for Java HashMap

Hash-based maps, particularly java.util.HashMap, are pervasive in Java applications and the JVM, making their performance critical. Evaluating optimizations is challenging because performance depends on factors such as operation patterns, key distributions, and resizing behavior. Microbenchmarks are fast and repeatable but often oversimplify workloads, failing to capture the realistic usage patterns. Application benchmarks (e.g., DaCapo, Renaissance) provide realistic usages but are more expensive to run, prone to variability, and dominated by non-HashMap computations, making map-related performance changes difficult to observe. To address this challenge, we propose MapReplay, a benchmarking methodology that combines the realism of application benchmarks with the efficiency of microbenchmarks. MapReplay traces HashMap API usages generating a replay workload that reproduces the same operation sequence while faithfully reconstructing internal map states. This enables realistic and efficient evaluation of alternative implementations under realistic usage patterns. Applying MapReplay to DaCapo-Chopin and Renaissance, the resulting suite, MapReplayBench, reproduces application-level performance trends while reducing experimentation time and revealing insights difficult to obtain from full benchmarks.

cs.PL↗

arXiv2023

Profiling and Optimizing Java Streams

The Stream API was added in Java 8 to allow the declarative expression of data-processing logic, typically map-reduce-like data transformations on collections and datasets. The Stream API introduces two key abstractions. The stream, which is a sequence of elements available in a data source, and the stream pipeline, which contains operations (e.g., map, filter, reduce) that are applied to the elements in the stream upon execution. Streams are getting popular among Java developers as they leverage the conciseness of functional programming and ease the parallelization of data processing. Despite the benefits of streams, in comparison to data processing relying on imperative code, streams can introduce significant overheads which are mainly caused by extra object allocations and reclamations, and the use of virtual method calls. As a result, developers need means to study the runtime behavior of streams in the goal of both mitigating such abstraction overheads and optimizing stream processing. Unfortunately, there is a lack of dedicated tools able to dynamically analyze streams to help developers specifically locate issues degrading application performance. In this paper, we address the profiling and optimization of streams. We present a novel profiling technique for measuring the computations performed by a stream in terms of elapsed reference cycles, which we use to locate problematic streams with a major impact on application performance. While accuracy is crucial to this end, the inserted instrumentation code causes the execution of extra cycles, which are partially included in the profiles. To mitigate this issue, we estimate and compensate for the extra cycles caused by the inserted instrumentation code. We implement our approach in StreamProf that, to the best of our knowledge, is the first dedicated stream profiler for the Java Virtual Machine (JVM). With StreamProf, we find that cycle profiling is effective to detect problematic streams whose optimization can enable significant performance gains. We also find that the accurate profiling of tasks supporting parallel stream processing allows the diagnosis of load imbalance according to the distribution of stream-related cycles at a thread level. We conduct an evaluation on sequential and parallel stream-based workloads that are publicly available in three different sources. The evaluation shows that our profiling technique is efficient and yields accurate profiles. Moreover, we show the actionability of our profiles by guiding stream-related optimizations on two workloads from Renaissance. Our optimizations require the modification of only a few lines of code while achieving speedups up to a factor of 5x. Java streams have been extensively studied by recent work, focusing on both how developers are using streams and how to optimize them. Current approaches in the optimization of streams mainly rely on static analysis techniques that overlook runtime information, suffer from important limitations to detect all streams executed by a Java application, or are not suitable for the analysis of parallel streams. Understanding the dynamic behavior of both sequential and parallel stream processing and its impact on application performance is crucial to help users make better decisions while using streams.

cs.PL↗

arXiv2021

Automatically Assessing and Extending Code Coverage for NPM Packages

Typical Node.js applications extensively rely on packages hosted in the npm registry. As such packages may be used by thousands of other packages or applications, it is important to assess their code coverage. Moreover, increasing code coverage may help detect previously unknown issues. In this paper, we introduce TESA, a new tool that automatically assembles a test suite for any package in the npm registry. The test suite includes 1) tests written for the target package and usually hosted in its development repository, and 2) tests selected from dependent packages. The former tests allow assessing the code coverage of the target package, while the latter ones can increase code coverage by exploiting third-party tests that also exercise code in the target package. We use TESA to assess the code coverage of 500 popular npm packages. Then, we demonstrate that TESA can significantly increase code coverage by including tests from dependent packages. Finally, we show that the test suites assembled by TESA increase the effectiveness of existing dynamic program analyses to identify performance issues that are not detectable when only executing the developer's tests.

cs.SE↗

arXiv2019

On Evaluating the Renaissance Benchmarking Suite: Variety, Performance, and Complexity

The recently proposed Renaissance suite is composed of modern, real-world, concurrent, and object-oriented workloads that exercise various concurrency primitives of the JVM. Renaissance was used to compare performance of two stateof-the-art, production-quality JIT compilers (HotSpot C2 and Graal), and to show that the performance differences are more significant than on existing suites such as DaCapo and SPECjvm2008. In this technical report, we give an overview of the experimental setup that we used to assess the variety and complexity of the Renaissance suite, as well as its amenability to new compiler optimizations. We then present the obtained measurements in detail.

cs.PL↗