Developer Resources from Intel and Ginkgo
Functionally porting the linear algebra functionality of Ginkgo from CUDA* to SYCL*-enabled devices allows multiarchitecture, cross-vendor programming with the library. It also lets you use Intel's latest architectures, including Intel® Iris® Xe graphics and Intel® Data Center GPU Max Series.
With SYCL-ported Ginkgo on an Intel Data Center GPU Max Series 1550:
- Its sparse Matrix-Vector (SpMV) on average performs 2x better than the Intel® oneAPI Math Kernel Library compressed sparse row (CSR) matrix-vector implementation. The enhancement can even reach 100x for problems from the SuiteSparse Matrix Collection.
- Its batched iterative solvers on Intel Data Center GPU Max Series 1550 (one GPU, a single-socket system) on average run 1.7x and 1.3x better than on NVIDIA* A100 and H100 GPUs, respectively. Intel Data Center GPU Max 1550 2s (two GPUs, a double-socket system) outperforms NVIDIA A100 and H100 GPUs by an average factor of 3.1 and 2.4 respectively.
Get Started with Ginkgo
Learn more about this high-performance, cross-platform, open source library for linear algebra through its GitHub* repository.
"Adding the Intel SYCL back end to the Ginkgo library gives domain scientists a high-performance math library for running workloads on the Intel GPUs."
— Hartwig Anzt, University of Tennessee
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Download the Stand-Alone Version
A stand-alone download of the Intel® oneAPI DPC++/C++ Compiler is available. You can download binaries from Intel or choose your preferred repository.
Download the Stand-Alone Version
A stand-alone download of the Intel® oneAPI Math Kernel Library is available. You can download binaries from Intel or choose your preferred repository.