Keyboard shortcuts

Press or to navigate between chapters

Press S or / to search in the book

Press ? to show this help

Press Esc to hide this help

Capability and Support Matrix

This page records which CPU and GPU architectures each PRISM-Q backend supports, and where distributed execution stands. CPU backends are written in portable Rust and run on every supported architecture; SIMD acceleration (AVX2/FMA/BMI2 on x86-64, NEON on ARM64) is selected at runtime where a kernel exists, otherwise a scalar path is used.

Legend

MarkMeaning
YesSupported
SIMDSupported with a dedicated SIMD-accelerated kernel on this architecture
ScalarRuns, but without a dedicated SIMD kernel (portable fallback)
NoNot available for this backend
PlannedNot implemented yet; on the roadmap

Backend support by architecture

Backendx86-64AVX2/FMA/BMI2ARM64NEONCUDA (NVIDIA)ROCm (AMD)Distributed
StatevectorYesSIMDYesSIMDYesPlannedPlanned
StabilizerYesSIMDYesSIMDYesPlannedPlanned
Factored StabilizerYesSIMDYesSIMDNoPlannedPlanned
SparseYesScalarYesScalarNoPlannedPlanned
MPSYesSIMDYesSIMDNoPlannedPlanned
Product StateYesScalarYesScalarNoPlannedPlanned
Tensor NetworkYesScalarYesScalarNoPlannedPlanned
FactoredYesSIMDYesSIMDNoPlannedPlanned
Stabilizer RankYesSIMDYesSIMDNoPlannedPlanned
Stochastic PauliYesScalarYesScalarNoPlannedPlanned
Deterministic PauliYesScalarYesScalarNoPlannedPlanned

Notes:

  • AVX2/FMA/BMI2 is the x86-64 SIMD tier. The active tier is chosen at runtime (AVX2+FMA, then FMA, then SSE2 baseline). See Threading, SIMD, and Memory Layout.
  • NEON is the ARM64 SIMD tier. Backends marked SIMD carry a NEON kernel that mirrors the x86-64 path; the rest fall back to scalar code on ARM64.
  • CUDA covers the optional gpu feature. Only the statevector and stabilizer paths have device kernels; every other backend runs on CPU. See the GPU Backend guide.

Not yet supported

TargetStatusNotes
ROCm (AMD GPU)PlannedNo AMD device kernels; the GPU path is CUDA-only
Distributed CPUPlannedNo multi-node execution
Distributed GPUPlannedNo multi-node GPU execution
Multi-GPUPlannedA GPU context binds a single device

These targets are listed so the matrix reflects the roadmap rather than hiding the gaps. Distributed execution is not a code path PRISM-Q exposes today.