concurrent threads. Julia compiles and uses its own copy of OpenBLAS, with threads currently capped at 8 (or the number of your cores). Modifying OpenBLAS settings or compiling Julia with a different BLAS library MKL.jl, a package that makes Julia's linear algebra use Intel MKL BLAS and LAPACK instead of OpenBLAS, or search the discussion forum for suggestions on how to set this up manually. Note that Intel elementary math functions. • [DSFMT] — fast Mersenne Twister pseudorandom number generator library. • [OpenBLAS] — fast, open, and maintained [basic linear algebra subprograms (BLAS)] • [LAPACK] — library of

矩阵乘法是深度学习计算的重要组成部分，我们利用 ARM 架构新提供的矩阵扩展对 bf16 类型的矩阵乘法计算进行优化，该优化将纯矩阵乘法的运算速度提升 3 倍以上，对深度学习推理任务性能提升明显。目前，该成果已经被集成进 OpenBLAS 和 PyTorch 中。 - 本次演讲，将向大家介绍我们在倚天 710 ARM 芯片上开展的 Python + AI 优化工作，以及在 ARM 云平台上部署 Python + AI 任务的最佳实践。 深度学习推理加速 ## • BF16 gemm 实现 • ARM Compute Library • OpenBLAS • TensorFlow • oneDNN + ACL • DNNL_DEFAULT_FPMATH_MODE=BF16 ## • PyTorch • OpenBLAS • oneDNN + ACL • torch.set_float32_fast_math_mode("BF16") ## 深度学习推理加速 • BF16 gemm 实现 • ARM Compute Library • OpenBLAS • TensorFlow • oneDNN + ACL • DNNL_DEFAULT_FPMATH_MODE=BF16 • PyTorch • OpenBLAS • oneDNN + ACL • torch.set_float32_fast_math_mode("BF16")

here's the metapackage for OpenBLAS: https://github.com/AnacondaRecipes/openblas-feedstock/blob/ae5af5e/recipe/meta.yaml#L127-L131 And the run_exports for OpenBLAS, as part of openblas-devel: https://github https://github.com/AnacondaRecipes/openblas-feedstock/blob/ae5af5e/recipe/meta.yaml#L100 Fundamentally, conda's model of mutual exclusivity relies on the package name. OpenBLAS and MKL are obviously not the same installing both at once. There's nothing stopping conda from installing NumPy with MKL and SciPy with OpenBLAS. The metapackage is what allows us to achieve the mutual exclusivity. It unifies the options on

here's the metapackage for OpenBLAS: https://github.com/AnacondaRecipes/openblas-feedstock/blob/ae5af5e/recipe/meta.yaml#L127-L131 And the run_exports for OpenBLAS, as part of openblas-devel: https://github https://github.com/AnacondaRecipes/openblas-feedstock/blob/ae5af5e/recipe/meta.yaml#L100 Fundamentally, conda's model of mutual exclusivity relies on the package name. OpenBLAS and MKL are obviously not the same installing both at once. There's nothing stopping conda from installing NumPy with MKL and SciPy with OpenBLAS. The metapackage is what allows us to achieve the mutual exclusivity. It unifies the options on

multithreading. Julia compiles and uses its own copy of OpenBLAS for linear algebra, whose number of threads is controlled by the environment variable OPENBLAS_NUM_THREADS. It can either be set as a command line value for the number of OpenBLAS threads (e.g. based on the platform, the Julia version, etc.). However, it is generally recommended to check and set the value manually. The OpenBLAS behavior is as follows: follows: If OPENBLAS_NUM_THREADS=1, OpenBLAS uses the calling Julia thread(s), i.e. it “lives in” the Julia thread that runs the computation. If OPENBLAS_NUM_THREADS=N>1, OpenBLAS creates and manages its

multithreading. Julia compiles and uses its own copy of OpenBLAS for linear algebra, whose number of threads is controlled by the environment variable OPENBLAS_NUM_THREADS. It can either be set as a command line value for the number of OpenBLAS threads (e.g. based on the platform, the Julia version, etc.). However, it is generally recommended to check and set the value manually. The OpenBLAS behavior is as follows: follows: • If OPENBLAS_NUM_THREADS=1, OpenBLAS uses the calling Julia thread(s), i.e. it "lives in" the Julia thread that runs the computation. • If OPENBLAS_NUM_THREADS=N>1, OpenBLAS creates and

multithreading. Julia compiles and uses its own copy of OpenBLAS for linear algebra, whose number of threads is controlled by the environment variable OPENBLAS_NUM_THREADS. It can either be set as a command line value for the number of OpenBLAS threads (e.g. based on the platform, the Julia version, etc.). However, it is generally recommended to check and set the value manually. The OpenBLAS behavior is as follows: follows: • If OPENBLAS_NUM_THREADS=1, OpenBLAS uses the calling Julia thread(s), i.e. it "lives in" the Julia thread that runs the computation. • If OPENBLAS_NUM_THREADS=N>1, OpenBLAS creates and

multithreading. Julia compiles and uses its own copy of OpenBLAS for linear algebra, whose number of threads is controlled by the environment variable OPENBLAS_NUM_THREADS. It can either be set as a command line value for the number of OpenBLAS threads (e.g. based on the platform, the Julia version, etc.). However, it is generally recommended to check and set the value manually. The OpenBLAS behavior is as follows: follows: • If OPENBLAS_NUM_THREADS=1, OpenBLAS uses the calling Julia thread(s), i.e. it "lives in" the Julia thread that runs the computation. • If OPENBLAS_NUM_THREADS=N>1, OpenBLAS creates and

multithreading. Julia compiles and uses its own copy of OpenBLAS for linear algebra, whose number of threads is controlled by the environment variable OPENBLAS_NUM_THREADS. It can either be set as a command line value for the number of OpenBLAS threads (e.g. based on the platform, the Julia version, etc.). However, it is generally recommended to check and set the value manually. The OpenBLAS behavior is as follows: follows: If OPENBLAS_NUM_THREADS=1, OpenBLAS uses the calling Julia thread(s), i.e. it “lives in” the Julia thread that runs the computation. If OPENBLAS_NUM_THREADS=N>1, OpenBLAS creates and manages its

multithreading. Julia compiles and uses its own copy of OpenBLAS for linear algebra, whose number of threads is controlled by the environment variable OPENBLAS_NUM_THREADS. It can either be set as a command line value for the number of OpenBLAS threads (e.g. based on the platform, the Julia version, etc.). However, it is generally recommended to check and set the value manually. The OpenBLAS behavior is as follows: follows: If OPENBLAS_NUM_THREADS=1, OpenBLAS uses the calling Julia thread(s), i.e. it “lives in” the Julia thread that runs the computation. If OPENBLAS_NUM_THREADS=N>1, OpenBLAS creates and manages its