Preface: Supercomputers rely on math libraries to efficiently handle the complex numerical computations required for scientific simulations and modeling. These libraries provide optimized routines for linear algebra, numerical analysis, and other mathematical operations, enabling supercomputers to perform these calculations much faster than with general-purpose code.

While math libraries are a crucial component, they are not the sole key to boosting overall AI performance on supercomputers. Supercomputers excel at AI due to their parallel processing capabilities, specialized hardware like GPUs and TPUs, and efficient memory management, not just the math libraries they use. Math libraries are essential for performing the calculations required by AI algorithms, but they rely on the underlying hardware architecture and software infrastructure of the supercomputer to deliver that performance.

Background: AMD rocBLAS 6.0.2 is a version of AMD’s library for Basic Linear Algebra Subprograms (BLAS) optimized for AMD GPUs within the ROCm platform. It provides high-performance, robust implementations of BLAS operations, similar to legacy BLAS but adapted for GPU execution using the HIP programming language. Specifically, version 6.0.2 is a point release that includes minor bug fixes to improve the stability of applications using AMD’s MI300 GPUs. It also introduces new driver features for system qualification on partner server offerings.

Using AMD rocBLAS and Intel MKL (2016 or later) together can be beneficial because MKL, while optimized for Intel CPUs, can sometimes perform suboptimally on AMD CPUs. rocBLAS, on the other hand, is specifically optimized for AMD GPUs and CPUs, providing a performance boost on AMD hardware.

Why Mix rocBLAS and MKL?

• rocBLAS: Optimized for AMD GPUs (and CPUs via ROCm stack).
• MKL: Optimized for Intel CPUs, but still useful for certain CPU-bound tasks.
• Mixing: You can selectively use each library for the operations where it performs best.

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