The engine diagonalizes the GRM once, rotates the common design and every unique task-specific covariate once, rotates phenotypes in blocks, and fits independent REML tasks in parallel.

Features

C++17 numerical core using oneMKL or OpenBLAS/LAPACKE.
GRM eigendecomposition once per task set.
AI-REML with a strong-Wolfe line search; no EM updates.
Per-task OLS-residual phenotype scaling inside the numerical core, with all estimates and the restricted likelihood restored to the input phenotype units.
Safeguarded quadratic zoom interpolation (central 96% of the bracket), bisection fallback, and a last-valid-improving-point fallback when strict Wolfe curvature cannot be reached because of numerical roundoff.
Signed standard-deviation parameterization with a separate one-sided KKT check for the (\sigma_g^2=0) boundary.
No explicit dense (P) matrix.
Variable numbers of extra fixed-effect covariates per task through a CSR index.
Satterthwaite t/F fixed-effect inference by default, with optional Kenward-Roger F tests and a joint test of task-specific covariates.
Atomic block output and safe resume/force semantics.
Generic Python CLI for manifest creation, validation, execution, provenance signatures, status, and result export.

The numerical core has no knowledge of cohorts, molecular assay types, or domain-specific variable names.

The line search starts at 1, expands by 1.618 up to the configured maximum step, and uses quadratic interpolation only when its stationary point lies at least 2% away from both bracket endpoints. An invalid interpolation falls back to bisection. If the evaluation limit is reached, the search accepts the last finite covariance-valid point that improved the likelihood; it reports line_search_failed only when no such point exists.

Repository layout

include/spectra_reml/  public C++ API
src/                   numerical core, batch I/O, and CLI
python/spectra_reml.py generic Python CLI
examples/example.py    fully synthetic input example
scripts/run_server.sh  generic Linux build/run wrapper
tests/                 numerical and file-contract tests
docs/FORMAT.md         binary and tabular file contract

Build with Intel oneMKL

Load the oneAPI environment first:

source /opt/intel/oneapi/setvars.sh

cmake -S . -B build-mkl \
  -DCMAKE_BUILD_TYPE=Release \
  -DREML_BLAS=MKL \
  -DMKL_INTERFACE=lp64 \
  -DMKL_LINK=dynamic \
  -DMKL_THREADING=sequential
cmake --build build-mkl --parallel
ctest --test-dir build-mkl --output-on-failure

For a nonstandard installation, locate MKLConfig.cmake and set its directory explicitly:

cmake -S . -B build-mkl \
  -DREML_BLAS=MKL \
  -DMKL_DIR=/path/to/mkl/latest/lib/cmake/mkl \
  -DMKL_THREADING=sequential

Sequential BLAS is recommended because independent tasks are already parallelized by OpenMP.

Build with OpenBLAS

cmake -S . -B build-openblas \
  -DCMAKE_BUILD_TYPE=Release \
  -DREML_BLAS=OPENBLAS \
  -DOpenBLAS_ROOT=/path/to/openblas \
  -DLAPACKE_ROOT=/path/to/lapacke
cmake --build build-openblas --parallel
ctest --test-dir build-openblas --output-on-failure

The configuration performs a real CBLAS/LAPACKE link check. It supports LAPACKE either inside OpenBLAS or in a separate library.

Quick start

Create a synthetic bundle:

python examples/example.py

Create a manifest for your own data:

python python/spectra_reml.py make-manifest \
  --manifest work/manifest.json \
  --grm-bin data/example.grm.bin \
  --grm-id data/example.grm.id \
  --base-x data/base_x.f64.bin \
  --phenotypes data/phenotypes.f64.bin \
  --extra-covariates data/extra_covariates.f32.bin \
  --tasks data/tasks.tsv \
  --extra-offsets data/extra_offsets.i64.bin \
  --extra-indices data/extra_indices.i32.bin \
  --output-dir work/blocks \
  --n-samples 3523 \
  --n-base-covariates 8 \
  --n-phenotype-rows 10000 \
  --n-extra-covariate-rows 2000

Validate, run, and export results:

python python/spectra_reml.py validate --manifest work/manifest.json

python python/spectra_reml.py run \
  --manifest work/manifest.json \
  --engine build-mkl/spectra_reml \
  --threads 28 \
  --blas-threads 1 \
  --block-size 256 \
  --resume

python python/spectra_reml.py finalize \
  --manifest work/manifest.json \
  --output work/results.tsv.gz

results.tsv.gz retains the complete per-task summary and stores the fixed-effect vector and row-wise packed lower covariance as JSON arrays.

Fixed-effect inference defaults to Satterthwaite. Select Kenward-Roger or turn inference off with --fixed-effect-test kenward-roger or --fixed-effect-test none. Coefficient-wise standard errors, statistics, denominator degrees of freedom, and p-values are exported as JSON arrays; tasks with extra covariates also report their joint F test in the summary columns.

Recovery and provenance

Each block is written as four files, with .complete renamed last. The Python layer adds run.signature.json, which binds the canonical manifest, engine SHA-256, numerical options, thread settings, and block size.

--resume reuses complete blocks only when the signature matches exactly.
--force invalidates the old signature before deleting old blocks and starting a new generation.
finalize refuses blocks that are not bound to the current manifest.
--dry-run does not mutate output state.

Threading

Use one BLAS thread with multiple outer task threads unless benchmarking shows otherwise:

export MKL_NUM_THREADS=1
export OPENBLAS_NUM_THREADS=1
export OMP_DYNAMIC=FALSE

Then set --threads to the physical cores allocated to the process.

See docs/FORMAT.md for the exact file contract.

License

BSD 3-Clause. See LICENSE.