Multi-language | BestHub

IT Services Circle

Oct 12, 2023 · Frontend Development

Integrating WASI into WebContainers: Enabling Multi‑Language Execution in the Browser

The article explains how StackBlitz's WebContainers, a browser‑based container environment, now fully integrates the WebAssembly System Interface (WASI), allowing near‑native speed, secure sandboxed execution of multiple languages such as Rust, Python, C/C++, and introducing new CLI tools and future language support.

FrontendMulti-languagePython

0 likes · 6 min read

Integrating WASI into WebContainers: Enabling Multi‑Language Execution in the Browser

AntTech

Jul 21, 2023 · Big Data

Fury: A High‑Performance Multi‑Language Serialization Framework with JIT Compilation and Zero‑Copy

Fury is a JIT‑compiled, zero‑copy multi‑language serialization framework that delivers up to 170× faster performance than Java’s native serialization, supports automatic cross‑language object graph serialization for Java, Python, C++, Go and JavaScript, and offers specialized protocols for high‑throughput big‑data and AI workloads.

FuryJITMulti-language

0 likes · 15 min read

Fury: A High‑Performance Multi‑Language Serialization Framework with JIT Compilation and Zero‑Copy

Java Architecture Diary

Jul 22, 2021 · Fundamentals

GraalVM 21.2 Highlights: Native Image, Compiler, and Multi‑Language Improvements

GraalVM 21.2 introduces major updates across its ecosystem, including new Gradle and Maven plugins for Native Image with JUnit 5 support, enhanced compiler optimizations, expanded Truffle language capabilities, improved JavaScript, Ruby, Python, LLVM, and upgraded tooling such as VisualVM and JFR integration.

Compiler OptimizationsGraalVMMulti-language

0 likes · 18 min read

GraalVM 21.2 Highlights: Native Image, Compiler, and Multi‑Language Improvements

Python Programming Learning Circle

Mar 8, 2021 · Operations

Using IPython and Jupyter for Multi‑language and Parallel Computing

This article explains how IPython and Jupyter notebooks support multi‑language execution, integrate Fortran via F2PY, and enable parallel and distributed computing with ipyparallel, illustrating practical magic commands, cluster setup, and performance considerations for scientific Python workflows.

IPythonJupyterMagic Commands

0 likes · 12 min read

Using IPython and Jupyter for Multi‑language and Parallel Computing