Introduction

Overview

CuTe DSL is a Python-based domain-specific language (DSL) designed for dynamic compilation of numeric and GPU-oriented code. Its primary goals are:

• Consistent with CuTe C++, allowing users to express GPU kernels with full control of the hardware.

• JIT compilation for both host and GPU execution.

• DLPack integration, enabling seamless interop with frameworks (e.g., PyTorch, JAX).

• JIT caching, so that repeated calls to the same function benefit from cached IR modules.

• Native types and type inference to reduce boilerplate and improve performance.

• Optional lower-level control, offering direct access to GPU backends or specialized IR dialects.

Decorators

CuTe DSL provides two main Python decorators for generating optimized code via dynamic compilation:

1. @jit — Host-side JIT-compiled functions

2. @kernel — GPU kernel functions

Both decorators can optionally use a preprocessor that automatically expands Python control flow (loops, conditionals) into operations consumable by the underlying IR.

@jit

Declares JIT-compiled functions that can be invoked from Python or from other CuTe DSL functions.

Decorator Parameters:

• preprocessor:

  • True (default) — Automatically translate Python flow control (e.g., loops, if-statements) into IR operations.

  • False — No automatic expansion; Python flow control must be handled manually or avoided.

Call-site Parameters:

• no_cache:

  • True — Disables JIT caching, forcing a fresh compilation each call.

  • False (default) — Enables caching for faster subsequent calls.

@kernel

Defines GPU kernel functions, compiled as specialized GPU symbols through dynamic compilation.

Decorator Parameters:

• preprocessor:

  • True (default) — Automatically expands Python loops/ifs into GPU-compatible IR operations.

  • False — Expects manual or simplified kernel implementations.

Kernel Launch Parameters:

• grid Specifies the grid size as a list of integers.

• block Specifies the block size as a list of integers.

• cluster Specifies the cluster size as a list of integers.

• smem Specifies the size of shared memory in bytes (integer).

Calling Conventions

Caller	Callee	Allowed	Compilation/Runtime
Python function	@jit	✅	DSL runtime
Python function	@kernel	❌	N/A (error raised)
@jit	@jit	✅	Compile-time call, inlined
@jit	Python function	✅	Compile-time call, inlined
@jit	@kernel	✅	Dynamic call via GPU driver or runtime
@kernel	@jit	✅	Compile-time call, inlined
@kernel	Python function	✅	Compile-time call, inlined
@kernel	@kernel	❌	N/A (error raised)