Vectorization offers potential speedups in codes with significant array-based computations—speedups that amplify the improved performance obtained through higher-level, parallel computations using threads and distributed execution on clusters. Key features for vectorization include tunable array sizes to reflect various processor cache and instruction capabilities and stride-1 accesses within inner loops.
[Sponsored Post] This webinar: “Getting Started with Llama 3 on AMD Radeon and Instinct GPUs” provides a guide to installing Hugging Face transformers, Meta’s Llama 3 weights, and the necessary dependencies for running Llama locally on AMD systems with ROCm™ 6.0.
In today’s fast-paced world, driven by demands for speed and efficiency, the field of clinical development has undergone a remarkable transformation. The way trials are being conducted has changed significantly with decentralized clinical trials (DCT) becoming mainstream and the collection of clinical data from wearables and other remote-monitoring devices becoming common practice. While these advances […]
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