GraalVM: metaprogramming inside a polyglot system (invited talk)

Bonetta, Daniele

doi:10.1145/3281074.3284935

Cited by 6 publications

(8 citation statements)

References 3 publications

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“…GraalVM native-image is a tool, built on top of the GraalVM compiler [8], to compile ahead-of-time applications into standalone executables, which are named native images. It supports JVM-based languages, e.g., Java, Scala, Clojure and Kotlin.…”

Section: Graalvm Native-imagementioning

confidence: 99%

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Montsalvat

Yuhala

Ménétrey

Felber

et al. 2021

Proceedings of the 22nd International Middleware Conference

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The popularity of the Java programming language has led to its wide adoption in cloud computing infrastructures. However, Java applications running in untrusted clouds are vulnerable to various forms of privileged attacks. The emergence of trusted execution environments (TEEs) such as Intel SGX mitigates this problem. TEEs protect code and data in secure enclaves inaccessible to untrusted software, including the kernel and hypervisors. To efficiently use TEEs, developers must manually partition their applications into trusted and untrusted parts, in order to reduce the size of the trusted computing base (TCB) and minimise the risks of security vulnerabilities. However, partitioning applications poses two important challenges: (i) ensuring efficient object communication between the partitioned components, and (ii) ensuring the consistency of garbage collection between the parts, especially with memory-managed languages such as Java. We present Montsalvat, a tool which provides a practical and intuitive annotation-based partitioning approach for Java applications destined for secure enclaves. Montsalvat provides an RMI-like mechanism to ensure inter-object communication, as well as consistent garbage collection across the partitioned components. We implement Montsalvat with GraalVM native-image, a tool for compiling Java applications ahead-of-time into standalone native executables that do not require a JVM at runtime. Our extensive evaluation with micro-and macro-benchmarks shows our partitioning approach to boost performance in real-world applications up to 6.6× (PalDB) and 2.2× (GraphChi) as compared to solutions that naively include the entire applications in the enclave.

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Section: Graalvm Native-imagementioning

confidence: 99%

“…We implemented our approach with GraalVM [8] and Intel SGX. GraalVM is a high-performance JDK distribution that makes it possible to build and run applications implemented in a wide range of high-level languages (e.g., Java, Scala, JavaScript, Clojure, Kotlin, etc.).…”

Section: Introductionmentioning

confidence: 99%

Montsalvat

Yuhala

Ménétrey

Felber

et al. 2021

Proceedings of the 22nd International Middleware Conference

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“…scenery runs on Windows, Linux, and macOS (G 4 ). scenery uses the low-level Vulkan API for fast and efficient rendering, and can fall back to an OpenGL 4.1-based renderer 3 .…”

Section: Scenerymentioning

confidence: 99%

“…The JVM is widely used, provides excellent dependency management via the Maven or Gradle build tools, and efficient, easy-to-use abstractions for, e.g., multithreading or UIs on different operating systems. Additionally, with the 2 See arivis.com/en/imaging-science/imaging-science, fei.com/software/amira/, and imaris.oxinst.com 3 The Vulkan renderer uses the LWJGL Vulkan bindings (see lwjgl.org), while the OpenGL renderer uses JOGL (see jogamp.org). 4 For this project, we have measured the timings of performancecritical parts of code, such as 4x4 matrix multiplication.…”

Section: Realtime Rendering On the Jvm -Gmentioning

confidence: 99%

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scenery: Flexible Virtual Reality Visualization on the Java VM

Günther

Pietzsch

Gupta

et al. 2019

2019 IEEE Visualization Conference (VIS)

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Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data from analysis of such data or simulations. Visualization is often the first step in making sense of data, and a crucial part of building and debugging analysis pipelines. It is therefore important that visualizations can be quickly prototyped, as well as developed or embedded into full applications. In order to better judge spatiotemporal relationships, immersive hardware, such as Virtual or Augmented Reality (VR/AR) headsets and associated controllers are becoming invaluable tools. In this work we introduce scenery, a flexible VR/AR visualization framework for the Java VM that can handle mesh and large volumetric data, containing multiple views, timepoints, and color channels. scenery is free and open-source software, works on all major platforms, and uses the Vulkan or OpenGL rendering APIs. We introduce scenery's main features and example applications, such as its use in VR for microscopy, in the biomedical image analysis software Fiji, or for visualising agent-based simulations.

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Midair: An Intermediate Representation for Multi-purpose Program Analysis

Menshikov

2020

Computational Science and Its Applications – ICCSA 2020

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The static analysis field had grown enough to be used not only for finding casual defects. In practice, it may be used to enforce the coding style and flag undesired syntax constructs, find logical mistakes, prove that the program satisfies its specification, apply domain-specific checks, or even verify cross-program compatibility. Those are all valid use cases that are required to be handled by the static analyzer, and the intermediate representation (IR) affects how can it be done. A typical compiler or analyzer uses a number of IRs, each of them helps with a specific problem. For our static analyzer project, we found that existing IRs partially do not match our requirements, which led to the creation of Midair—an IR for multi-purpose program analysis. It is positioned right between IRs created primarily for the compilation (like LLVM, MLIR, GIMPLE) and verification IRs (such as Boogie) with the hope that it would be both close to a low level and suitable for verification while applying to practical analysis tools. The IR consists of 4 layers, allowing for a transparent transformation between forms saving time and space. A flexible type system supporting non-machinery types and an ability to augment the representation with external metadata provided by solvers had been added. The application of Midair to our analysis framework uncovered advantages and non-critical issues, which are planned to be worked around.

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GraalVM: metaprogramming inside a polyglot system (invited talk)

Cited by 6 publications

References 3 publications

Montsalvat

Montsalvat

scenery: Flexible Virtual Reality Visualization on the Java VM

Midair: An Intermediate Representation for Multi-purpose Program Analysis

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