Changelog

All notable changes to Raster will be documented in this file.

0.1.x (2026-04-10) -- Initial Release

Raster's first public release. Functional scientific computing for Clojure with typed multiple dispatch, automatic differentiation, and GPU compilation on the JVM.

Core

• Typed multiple dispatch (deftm, ftm) with Julia-style method specialization
• Walker devirtualization resolves dispatch at definition time
• JDK ClassFile API bytecode emission for zero-overhead compiled functions
• Lazy JIT compilation on first call with low overhead
• compile-aot for AOT-style whole-program inlining
• Polymorphic arithmetic (raster.numeric) for all JVM numeric types
• Math library (raster.math) with trig, hyperbolic, exp/log, rounding, FMA
• Polymorphic arrays (raster.arrays) with type-dispatched aget/aset/alength
• Type promotion lattice with automatic widening
• Abstract type hierarchy (Real, AbstractFloat, AbstractInteger, Signed)
• Algebraic type lattice (Semigroup, Monoid, Group, Ring, Field)
• Value types (defvalue) with Valhalla JDK 27 support
• Parametric types ((All [T])) for generic value types (Dual, SparseVector, etc.)
• Float16 value type (Valhalla JDK 27)
• Complex numbers (raster.types.complex)
• Trait system (raster.traits) for Julia-style trait dispatch

Compiler Pipeline

• Nanopass architecture with well-defined IR dialects and pass contracts
• Inlining with safe-to-inline checks and parametric specialization
• Dead code elimination with beichte purity analysis
• Partial evaluation and constant folding
• Buffer fusion via escape analysis (zero heap allocations in hot path)
• SOAC fusion (Futhark-style map/reduce/scan chain fusion)
• SIMD vectorization via JDK Vector API
• explain-pipeline for transparent compiler diagnostics

Automatic Differentiation

• Forward-mode AD (raster.ad.forward) with parametric Dual numbers
• Reverse-mode AD (raster.ad.reverse) via IR-level source transformation
• Composable AD -- value+grad, grad as first-class compiler-visible functions
• rrule registry for custom reverse-mode rules
• AD templates for flat codegen without closure overhead

GPU Computing

• Parallel primitives (raster.par) -- map, reduce, scan, stencil, broadcast
• OpenCL ICD backend (NVIDIA, AMD, Intel) via Panama FFM
• Intel Level Zero backend with unified shared memory
• Vulkan compute backend (GLSL 450 -> SPIR-V)
• SoA memory layout for GPU-friendly access patterns
• Unified GPU session API (raster.gpu)

Scientific Computing

• ODE solvers -- Euler, RK4, Dormand-Prince 5, Tsitouras 5, Implicit Euler, Rosenbrock23
• PDE solvers -- method-of-lines with finite-difference stencils
• SDE solvers -- Euler-Maruyama
• Sensitivity analysis via forward-mode AD through ODE integration
• Optimization -- L-BFGS, Nelder-Mead, Newton, gradient descent, Adam
• Root finding -- bisection, Brent, Newton, fixed-point iteration
• Numerical integration -- Gauss-Kronrod, Simpson
• Interpolation -- linear, cubic spline, Akima, PCHIP, 2D bilinear/bicubic
• Signal processing -- FFT, PSD, convolution, windows (Hann, Hamming, Blackman, Kaiser)
• Special functions -- gamma, beta, erf, Bessel
• Distributions -- Normal, Uniform, Exponential, Gamma, Poisson, Beta, Binomial
• Statistics -- t-test, chi-squared, F-test, KS test, correlation, descriptive stats

Linear Algebra

• Dense fixed-size -- Vec2/3/4, Mat2x2/3x3/4x4 with value semantics
• Dense general -- LU, Cholesky, solve
• LAPACK via Panama FFI (OpenBLAS cblas_dgemv/sgemv)
• Decompositions -- SVD, QR, eigendecomposition
• Iterative solvers -- CG, GMRES, BiCGSTAB, Lanczos
• Sparse vectors/matrices -- SparseVector, CSR, COO (parametric)
• PCA

Deep Learning

• Layers -- linear, conv1d, conv2d, maxpool2d, layer-norm, group-norm, batch-norm
• Activations -- relu, silu, gelu, sigmoid, tanh, leaky-relu
• Loss functions -- MSE, cross-entropy, Huber, L1
• Optimizers -- SGD, Adam, AdamW with gradient clipping and LR schedulers
• Einsum and einops-style rearrangement
• Attention -- scaled dot-product, multi-head, graph attention

Symbolic Computation

• Symbolic expressions (raster.sym.core) with Sym value type
• Symbolic differentiation and Taylor series expansion
• Linearity/sparsity analysis
• Function algebra (D operator, composition)

Geometric Algebra

• Cl(p,q,r) multivectors with geometric, outer, inner products
• Hodge dual, reversal, grade projection
• Valhalla value class generation for compiled GA types

Other

• Agent-based simulation (raster.abm) with GPU compilation
• Vulkan rendering engine (raster.vk) with mesh, texture, audio support
• Game examples -- Valley (voxel survival), Asteroids, Doom-style renderer

Known Limitations

• JDK 24+ required for ClassFile API and Panama FFM
• Valhalla JDK 27 recommended for value types, Float16, and best performance
• GraalVM JDK gives 3-6x slower results than Valhalla HotSpot C2
• Float16 math functions (sin, cos, exp, etc.) not yet specialized -- computed via Float32
• GPU compound kernels have a known barrier synchronization issue
• deftm does not support & {:keys [...]} keyword args directly
• Functions with >4 args cannot have primitive type hints (Clojure limitation)