Registry / ai-ml / onemkl-sycl-sparse

onemkl-sycl-sparse

library2026.0.0pypypiunverified

Intel oneAPI Math Kernel Library (oneMKL) Sparse BLAS routines for SYCL devices. This package provides optimized sparse linear algebra operations (e.g., sparse matrix-vector multiply, sparse triangular solvers) on Intel GPUs and CPUs using SYCL. Current version: 2026.0.0. Released quarterly as part of Intel's oneAPI toolkit.

ai-ml data

pip install onemkl-sycl-sparse

package ↗homepage ↗

INSTALL

IMPORT

SIG · ONEMKL-SYCL-SPARSE

onemkl-sycl-sparse

ai-mlpythonv2026.0.0

harness data pending

Install & Compatibility

Where this runs

No compatibility data collected yet for this library.

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

sparse

✓ import onemkl_sycl_sparse

✗ from onemkl_sycl_sparse import sparse

Performs a sparse matrix-vector multiply using CSR format on a SYCL GPU device.

import dpctl import numpy as np from onemkl_sycl_sparse import sparse # Create a SYCL queue (device selector: 0 for gpu, 1 for cpu) queue = dpctl.SyclQueue("gpu") # Create a simple CSR matrix row = np.array([0, 0, 1, 2], dtype=np.int64) col = np.array([0, 1, 1, 2], dtype=np.int64) val = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float64) nrows, ncols = 3, 3 # Allocate USM memory d_row = dpctl.tensor.usm_ndarray(row, dtype=row.dtype, queue=queue) d_col = dpctl.tensor.usm_ndarray(col, dtype=col.dtype, queue=queue) d_val = dpctl.tensor.usm_ndarray(val, dtype=val.dtype, queue=queue) # Create handle and perform operation handle = sparse.create_handle(queue) sparse_matrix = sparse.init_csr_matrix(handle, nrows, ncols, d_row, d_col, d_val) # Sparse matrix-vector multiply x = np.array([1.0, 2.0, 3.0], dtype=np.float64) d_x = dpctl.tensor.usm_ndarray(x, dtype=x.dtype, queue=queue) d_y = dpctl.tensor.empty(3, dtype=np.float64, queue=queue) sparse.omatadd(handle, sparse_matrix, sparse_matrix, 1.0, 1.0, d_x, d_y) y = d_y.asnumpy() print(y) sparse.destroy_handle(handle)

Debug

Known issues

gotchaAll input arrays (row, col, val) must be on the same SYCL device as the queue. Do not mix host memory (numpy) directly without copying to USM.

fix

Use dpctl.tensor.usm_ndarray to allocate device memory and copy data.

affects: all

gotchaMatrix handles must be explicitly created and destroyed. Forgetting sparse.destroy_handle leads to memory leaks.

fix

Always pair sparse.create_handle with sparse.destroy_handle, ideally in a try-finally block or context manager (if available).

affects: all

gotchaThe sparse module expects Fortran-style (column-major) ordering for some internal operations, but CSR arrays are typically row-major. Ensure row-index array is sorted and unique per row.

fix

Pre-process CSR arrays to be in sorted column order per row (e.g., using scipy sparse CSR construction).

affects: all

deprecatedThe old import path from 'oneapi.mkl' is deprecated and removed in 2026.0.0.

fix

Use 'from onemkl_sycl_sparse import sparse'.

affects: <2026.0.0

Upgrade

Version history

2026.0.0latest on PyPI · released Apr 24, 2026

Audit

Dependencies

numpyoptionalArray compatibility and data type conversions

dpctlrequiredSYCL device management and USM allocation

dpnpoptionalNumPy-like API on SYCL devices (optional, but recommended for ease of use)

Agent activity

2 hits · last 30 days

node

Resources

packagehttps://pypi.org/project/onemkl-sycl-sparse/ ↗

homepagehttps://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl.html ↗