Search Results for "jit"
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Showing 62 open source projects for "jit"
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1
UTM
Virtual machines for iOS and macOS
...As a result, UTM SE does not require jailbreaking or any JIT workarounds and can be sideloaded as a regular app. -
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Criterium
Benchmarking library for clojure
Criterium is a robust benchmarking library for Clojure that addresses common statistical and JIT-related issues. It provides accurate timings through warm-up, garbage collection control, and statistical summaries—making microbenchmarking more reliable than using time. Statistical processing of multiple evaluations. Inclusion of a warm-up period, designed to allow the JIT compiler to optimise its code. Purging of gc before testing, to isolate timings from GC state prior to testing. ... -
3
Mercurius
Implement GraphQL servers and gateways with Fastify
Mercurius is a GraphQL adapter for Fastify. Caching of query parsing and validation. Automatic loader integration to avoid 1 + N queries. Just-In-Time compiler via graphql-jit. Federation support via @mercuriusjs/federation, including Subscriptions. Gateway implementation via @mercuriusjs/gateway, including Subscriptions. Batched query support. Customisable persisted queries. -
4
XLS
XLS: Accelerated HW Synthesis
...At the front end, DSLX lets you describe algorithms with strong typing and familiar control flow while remaining synthesis-friendly. The compiler lowers DSLX into a rich intermediate representation, applies aggressive optimization and scheduling passes, and can either JIT the design for software simulation or emit Verilog for FPGA/ASIC flows. A key idea is “software-style” iteration: fast, deterministic simulation via the JIT encourages test-driven development and property checking before committing to RTL. XLS also provides tooling for pipelining, state insertion, and formal equivalence checks between different stages, giving developers confidence as designs evolve. ... -
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5
Fury
A blazing fast multi-language serialization framework
Fury is a blazing-fast multi-language serialization framework powered by jit(just-in-time compilation) and zero-copy, providing up to 170x performance and ultimate ease of use. Based on efficient JIT, struct deserialization can get 170x speed up compared to other serialization frameworks. In serialization scenarios, Fury can achieve a 100x speed up compared to traditional serialization frameworks. If you use Java clusters on a large scale, this will save a lot of computing resources. -
6
Wasm3
A fast WebAssembly interpreter and the most universal WASM runtime
Wasm3 is a highly optimized WebAssembly interpreter designed to execute .wasm binaries efficiently across a wide range of platforms, including embedded systems, IoT devices, and constrained environments. Unlike JIT-based runtimes, Wasm3 uses an interpreter architecture that prioritizes portability, low memory usage, and predictable execution, making it especially suitable for environments where resources are limited. It is widely regarded as one of the fastest interpreters for WebAssembly, achieving competitive performance benchmarks despite not relying on compilation techniques. ... -
7
CUDA Python
Performance meets Productivity
...It acts as a metapackage composed of multiple submodules that provide both high-level and low-level access to CUDA functionality, including runtime APIs, driver APIs, and JIT compilation tools. The project is designed to simplify GPU programming by offering Pythonic abstractions while still exposing the full power of CUDA for advanced users. It integrates tightly with the broader Python GPU ecosystem, including Numba for kernel compilation and CCCL for parallel primitives, allowing developers to write performant code without leaving Python. ... -
8
Flax
Flax is a neural network library for JAX
...Its design separates pure computation from state by threading parameter collections and RNGs explicitly, enabling reproducibility, transformation, and easy experimentation with JAX transforms like jit, pmap, and vmap. Modules define parameterized computations, but initialization and application remain side-effect free, which pairs naturally with JAX’s staging and compilation model. Flax emphasizes composability: optimizers, training loops, and checkpointing are provided as examples or utilities rather than monolithic frameworks, encouraging research-friendly customization. ... -
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Wasmi
Efficient and versatile WebAssembly interpreter for embedded systems
wasmi is a lightweight WebAssembly interpreter written in Rust that is designed to execute Wasm binaries efficiently in constrained or embedded environments. Unlike JIT-based runtimes, wasmi focuses on interpretation, prioritizing portability, determinism, and low resource usage over maximum raw performance. It is particularly well-suited for blockchain systems, embedded devices, and other contexts where predictable execution and minimal dependencies are critical. The runtime implements the WebAssembly specification and provides a safe execution environment with strict sandboxing, ensuring that untrusted code cannot access host resources without explicit permission. wasmi also emphasizes modularity and embeddability, allowing developers to integrate it into their applications with fine-grained control over execution behavior. ... -
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Numba CUDA Target
The CUDA target for Numba
Numba CUDA Target is NVIDIA’s maintained CUDA backend for the Numba JIT compiler, enabling developers to write GPU-accelerated code directly in Python. It allows users to define CUDA kernels using Python syntax, which are then compiled into efficient GPU code at runtime using LLVM-based toolchains. This approach significantly lowers the barrier to entry for GPU programming by eliminating the need to write CUDA C++ while still delivering high performance. -
11
Tunix
A JAX-native LLM Post-Training Library
Tunix is a JAX-native library for post-training large language models, bringing supervised fine-tuning, reinforcement learning–based alignment, and knowledge distillation into one coherent toolkit. It embraces JAX’s strengths—functional programming, jit compilation, and effortless multi-device execution—so experiments scale from a single GPU to pods of TPUs with minimal code changes. The library is organized around modular pipelines for data loading, rollout, optimization, and evaluation, letting practitioners swap components without rewriting the whole stack. Examples and reference configs demonstrate end-to-end runs for common model families, helping teams reproduce baselines before customizing. ... -
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WebAssembly Micro Runtime
WebAssembly Micro Runtime (WAMR)
...Developed under the Bytecode Alliance, WAMR emphasizes a small memory footprint and high configurability, enabling it to operate effectively even in constrained hardware environments. The runtime supports multiple execution modes, including interpreter, Ahead-of-Time (AOT) compilation, and Just-in-Time (JIT) compilation, allowing developers to balance performance and portability depending on their needs. WAMR also provides near-native execution speed through its optimized compilation strategies, making it suitable for performance-critical applications. It includes a modular architecture with components such as a runtime core, an AOT compiler, and application frameworks that facilitate real-world deployment scenarios. -
13
toBeBetterJavaer
An easy-to-understand and humorous Java study guide
...Dedicated material explores multithreading, concurrency utilities, the Java Memory Model, locks, thread pools, and concurrent collections. JVM coverage includes bytecode, class loading, runtime memory, garbage collection, JIT compilation, and performance diagnosis. It also expands into Spring, databases, Redis, distributed systems, Linux, and other enterprise-development subjects. -
14
Proxy
Proxy: Next Generation Polymorphism in C++
...There are many drawbacks in this mechanism, including life management (because each object may have a different size and ownership) and reflection (because it is hard to balance between usability and memory allocation). To workaround these drawbacks, some languages like Java or C# choose to sacrifice performance by introducing GC to facilitate lifetime management, and JIT-compile the source code at runtime to generate full metadata. We improved the theory and implemented it as a C++ library without sacrificing performance, proposing to merge it into the C++ standard. -
15
Numba
NumPy aware dynamic Python compiler using LLVM
Numba is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code. Numba translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. Numba-compiled numerical algorithms in Python can approach the speeds of C or FORTRAN. You don't need to replace the Python interpreter, run a separate compilation step, or even have a C/C++ compiler installed. -
16
RLax
Library of JAX-based building blocks for reinforcement learning agents
...Rather than implementing full algorithms, RLax focuses on the core functional operations that underpin RL methods—such as computing value functions, returns, policy gradients, and loss terms—allowing researchers to flexibly assemble their own agents. It supports both on-policy and off-policy learning, as well as value-based, policy-based, and model-based approaches. RLax is fully JIT-compilable with JAX, enabling high-performance execution across CPU, GPU, and TPU backends. The library implements tools for Bellman equations, return distributions, general value functions, and policy optimization in both continuous and discrete action spaces. It integrates seamlessly with DeepMind’s Haiku (for neural network definition) and Optax (for optimization), making it a key component in modular RL pipelines. -
17
RustPython
A Python Interpreter written in Rust
RustPython is an implementation of the Python programming language written in Rust. Its goal is to provide a fast, embeddable, and secure interpreter that can be integrated into Rust applications or used standalone. Because it’s in Rust, it benefits from memory safety and modern tooling, allowing developers to compile Python into efficient binaries or embed it as a scripting engine in Rust projects. The interpreter aims to support the Python standard library, dynamic typing, garbage... -
18
Halide
A language for fast, portable data-parallel computation
...It isn't a standalone programming language however; rather it is embedded in C++ which means that you write C++ code, building an in-memory representation of a Halide pipeline using Halide's C++ API. This representation can then be compiled to an object file, or a JIT-compile and run in the same process. Halide also comes with a Python binding, allowing the writing of Halide embedded in Python without C++. -
19
LÖVR
Lua Virtual Reality engine
...Out of the box you get fast stereo rendering, VR controllers, hand tracking, haptics, spatial audio, 3D physics, PBR materials, SDF fonts, multiplayer, and more. The entire engine is 1MB and runs on LuaJIT, the fastest JIT compiler round these parts. Projects are just folders with scripts and assets in them, organized however you want. -
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Python Outlier Detection
A Python toolbox for scalable outlier detection
PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as outlier detection or anomaly detection. PyOD includes more than 30 detection algorithms, from classical LOF (SIGMOD 2000) to the latest COPOD (ICDM 2020) and SUOD (MLSys 2021). Since 2017, PyOD [AZNL19] has been successfully used in numerous academic researches and commercial products [AZHC+21, AZNHL19]. PyOD has multiple neural... -
21
Jittor
Jittor is a high-performance deep learning framework
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. ... -
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TapeWormBF
A JIT Brainfuck interpreter written in C++
TapeWorm is an optimising multipass brainfuck interpreter for Linux and Windows, written in C++. It produces and executes handwritten x86 assembly using asmjit as a runtime assembler. No AI was used at any point during development. -
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Objeck
Simple ⇒ Object-Oriented ⇒ Functional
Objeck is an object-oriented programming language with functional features. Objeck emphasizes, expression, simplicity, portability, and scalability. The programming environment consists of a compiler, virtual machine, and command line debugger with IDE plugins. -
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Shumai
Fast Differentiable Tensor Library in JavaScript & TypeScript with Bun
Shumai is an experimental differentiable tensor library for TypeScript and JavaScript, developed by Facebook Research. It provides a high-performance framework for numerical computing and machine learning within modern JavaScript runtimes. Built on Bun and Flashlight, with ArrayFire as its numerical backend, Shumai brings GPU-accelerated tensor operations, automatic differentiation, and scientific computing tools directly to JavaScript developers. It allows seamless integration of machine... -
25
PI-Based Image Encoder / Converter
Python code able to convert / compress image to PI (3.14, π) Indexes
...ZIP also include 16 MB file with 16,7 mil numbers of PI Benchmark(Single-Thread): Hardware & Environment Apple Silicon: Apple M2 (Mac mini/MacBook) x86_64 Platform: Intel Core Ultra 5 225F (Arrow Lake, 10 Cores) OS 1: Fedora 43 (GNOME) OS 2: Windows 11 Pro (23H2/24H2) Software: Python 3.14.3 + Numba JIT (latest) Results (Lower is better) Platform / OS CPU Time (Seconds) macOS (Native) Apple M2 52.151311 s (in default setup) Fedora Linux Intel Core Ultra 5 225F 58.536457 s (in default Power Management: Balanced) Windows 11 Intel Core Ultra 5 225F 59.681427 s (important! Power Management set as High Performance .. in Balanced is slow)
