arXiv subjects

Lubomír Bulej

Publications and source records attributed to Lubomír Bulej.

5 recordsLinked to original sources

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↗

arXiv2020

Managing Latency in Edge-Cloud Environment

Modern Cyber-physical Systems (CPS) include applications like smart traffic, smart agriculture, smart power grid, etc. Commonly, these systems are distributed and composed of end-user applications and microservices that typically run in the cloud. The connection with the physical world, which is inherent to CPS, brings the need to operate and respond in real-time. As the cloud becomes part of the computation loop, the real-time requirements have to be also reflected by the cloud. In this paper, we present an approach that provides soft real-time guarantees on the response time of services running in cloud and edge-cloud (i.e., cloud geographically close to the end-user), where these services are developed in high-level programming languages. In particular, we elaborate a method that allows us to predict the upper bound of the response time of a service when sharing the same computer with other services. Importantly, as our approach focuses on minimizing the impact on the developer of such services, it does not require any special programming model nor limits usage of common libraries, etc.

cs.DC↗

arXiv2020

Duet Benchmarking: Improving Measurement Accuracy in the Cloud

We investigate the duet measurement procedure, which helps improve the accuracy of performance comparison experiments conducted on shared machines by executing the measured artifacts in parallel and evaluating their relative performance together, rather than individually. Specifically, we analyze the behavior of the procedure in multiple cloud environments and use experimental evidence to answer multiple research questions concerning the assumption underlying the procedure. We demonstrate improvements in accuracy ranging from 2.3x to 12.5x (5.03x on average) for the tested ScalaBench (and DaCapo) workloads, and from 23.8x to 82.4x (37.4x on average) for the SPEC CPU 2017 workloads.

cs.PF↗

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↗