Search Results for "jit compiler"
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Showing 41 open source projects for "jit compiler"
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JITWatch
Log analyser / visualiser for Java HotSpot JIT compiler
Log analyser / visualiser for Java HotSpot JIT compiler. Inspect inlining decisions, hot methods, bytecode, and assembly. View results in the JavaFX user interface. -
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XLS
XLS: Accelerated HW Synthesis
XLS is an open-source toolkit for building high-level hardware with a modern compiler stack that spans from a functional DSL to optimized IR and hardware generation. 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. ... -
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Torch-TensorRT
PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
Torch-TensorRT is a compiler for PyTorch/TorchScript, targeting NVIDIA GPUs via NVIDIA’s TensorRT Deep Learning Optimizer and Runtime. Unlike PyTorch’s Just-In-Time (JIT) compiler, Torch-TensorRT is an Ahead-of-Time (AOT) compiler, meaning that before you deploy your TorchScript code, you go through an explicit compile step to convert a standard TorchScript program into a module targeting a TensorRT engine. -
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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. -
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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. ... -
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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. -
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LatticeQCD.jl
A native Julia code for lattice QCD with dynamical fermions in 4D
This code enables you to perform lattice QCD calculations! A native Julia code for Lattice QCD. -
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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. -
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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. -
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Gridap.jl
Grid-based approximation of partial differential equations in Julia
Gridap provides a set of tools for the grid-based approximation of partial differential equations (PDEs) written in the Julia programming language. The library currently supports linear and nonlinear PDE systems for scalar and vector fields, single and multi-field problems, conforming and nonconforming finite element (FE) discretizations, on structured and unstructured meshes of simplices and n-cubes. It also provides methods for time integration. Gridap is extensible and modular. One can... -
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BetaML.jl
Beta Machine Learning Toolkit
...If your favorite option or model is missing, you can try to implement it yourself and open a pull request to share it (see the section Contribute below) or request its implementation. Thanks to its JIT compiler, Julia is indeed in the sweet spot where we can easily write models in a high-level language and still have them running efficiently. -
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