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Vectorizes the following functions in ggml.c on x86:
ggml_fp16_to_fp32_row: using F16C intrinsics.
ggml_fp32_to_fp16_row: using F16C intrinsics.
ggml_bf16_to_fp32_row: using AVX2 and AVX512F intrinsics.

Aaron added 4 commits October 11, 2025 12:07
…e across SIMD registers and store in vector-sized chunks, while retaining the scalar tail for leftover elements and non-SIMD builds.
Vectorized the following functions in ggml.c for improved performance on x86 architectures:
- ggml_fp16_to_fp32_row: using F16C intrinsics.
- ggml_fp32_to_fp16_row: using F16C intrinsics.
- ggml_bf16_to_fp32_row: using AVX2 and AVX512F intrinsics.

This change follows the existing pattern of using direct SIMD intrinsic checks in this file.
@sirus20x6 sirus20x6 closed this Oct 15, 2025
@github-actions github-actions bot added the ggml changes relating to the ggml tensor library for machine learning label Oct 15, 2025
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