Just-in-time compilation
Just-in-time compilation translates or optimizes program code during execution, often using runtime information to generate specialized executable instructions.
Deep Learning with JAX
Compilation performed at runtime so code can be specialized and optimized for actual inputs and hardware.
- How it is used
- Speeds up large numerical computations and neural-network training across CPUs, GPUs, and TPUs.
- How deep it goes
- Explains decorators, tracing, Jaxpr, XLA, caching, static arguments, control flow, AOT compilation, and practical limitations.
- Examples
- Demonstrates SELU timing, MNIST training updates, static distance arguments, class-method helpers, and a moving-average exercise.
- What you can do after
- Apply it via jax.jit or @jax.jit, benchmark warmed-up executions, and diagnose purity, static-argument, and method errors.
Read how Deep Learning with JAX explains just-in-time compilation →
.NET in Action, Second Edition
A just-in-time compiler translates intermediate code into machine code during program execution.
- How it is used
- Explains how CoreCLR turns architecture-neutral CIL into executable machine code and why compilation timing matters for startup and benchmarks.
- How deep it goes
- Defines runtime compilation and describes selective and tiered behavior, but stops short of compiler implementation details and AOT instructions.
- Examples
- Uses a BenchmarkDotNet JSON-deserialization comparison whose pilot and warmup phases allow time for JIT compilation.
- What you can do after
- Evaluate/understand it when reasoning about startup costs and interpreting BenchmarkDotNet measurements.
Read how .NET in Action, Second Edition explains just-in-time compilation →
Julia as a Second Language
A just-in-time compiler compiles program code into executable machine code during program execution.
- How it is used
- Positions JIT compilation as part of Julia's combination of dynamic typing and high performance.
- How deep it goes
- Defines compilation timing and explains type-informed optimization, but does not walk through generated machine code.
- Examples
- Uses short Python and Bash scripts to illustrate startup costs and a Vec2D method to illustrate runtime type-checking overhead.
- What you can do after
- Evaluate/understand it when weighing Julia's faster post-compilation execution against slower startup for short scripts.
Read how Julia as a Second Language explains just-in-time compilation →
Deep Learning with R, Third Edition
Just-in-time compilation is the compilation of program code during execution into an optimized form for subsequent execution.
- How it is used
- Accelerates TensorFlow and JAX functions during execution after expressing them as compilable computations.
- How deep it goes
- Demonstrates function wrapping and an explicit JIT option, but does not explain compiler internals or optimization details.
- Examples
- Uses a TensorFlow dense-layer function with tf_function() and jit_compile = TRUE, plus JAX setup for analogous compilation.
- What you can do after
- Apply it via TensorFlow by wrapping a numerical function with tf_function() and enabling jit_compile when requested.
Read how Deep Learning with R, Third Edition explains just-in-time compilation →
Modern Angular
Just-in-time compilation is compilation performed during application startup or execution rather than entirely before deployment.
- How it is used
- Supports rapid Angular development by compiling templates through ng serve before browser rendering.
- How deep it goes
- Explains template-to-JavaScript compilation, development trade-offs, and AOT contrast, but omits compiler internals.
- Examples
- Uses ng serve for JIT development and ng build for a precompiled production application.
- What you can do after
- Evaluate when Angular development should use JIT and when deployment should use ahead-of-time compilation.
Experimentation for Engineers
A just-in-time compiler translates code into machine code during program execution rather than exclusively before execution.
- How it is used
- Optimizes JIT configuration to reduce CPU time for production-like web-server workloads.
- How deep it goes
- Explains configurable behaviors and measurement workflow, but stops short of compiler implementation internals.
- Examples
- Uses a seven-parameter web server, replayed production requests, a CPU-time simulator, and noisy offline measurements.
- What you can do after
- Operate/configure a JIT and evaluate parameter sets using production-like CPU-time measurements.
Read how Experimentation for Engineers explains just-in-time compilation →
Deep Learning with PyTorch, Second Edition
A compilation strategy that performs code optimization or translation during execution rather than before runtime.
- How it is used
- Deploys eager-mode PyTorch models as optimized graphs for production performance.
- How deep it goes
- Explains tracing, scripting, TorchScript, and dynamic capture, stopping short of compiler internals.
- Examples
- Uses a conditional PyTorch model, export failure, torch.compile, and original-versus-compiled output comparison.
- What you can do after
- Apply it via torch.compile to compile an eager-mode model and compare its compiled output with the original.
Read how Deep Learning with PyTorch, Second Edition explains just-in-time compilation →
Related concepts
- JavaScript engineused by · A JavaScript engine is software that parses, compiles or interprets, and executes JavaScript code.
- Juliaused by · Julia is a high-level, high-performance programming language designed for numerical, scientific, and technical computing.
- machine languageproduces · Machine language is a processor-specific instruction language encoded in a form that computer hardware can execute directly.
- ahead-of-time compilationcontrasts with · Ahead-of-time compilation is the translation of program code into executable or loadable form before program execution begins.
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