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.
Develop in the Cloud
Build and optimize oneAPI multiarchitecture applications using the latest Intel-optimized oneAPI and AI Tools, and test your workloads across Intel CPUs and GPUs. No hardware installations, software downloads, nor configuration is necessary.