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Summary: - The current Faiss conda recipe does not specify the OpenBLAS threading variant. - On Linux AArch64, the dependency solver selects the pthreads build. - This causes problems when Faiss calls BLAS from within an existing OpenMP parallel region due to nested threading - As a result, the benchmark below (with an OOB conda install of faiss) only yields 7% of the throughput reachable when an OMP build of OpenBLAS is selected (what this PR does) - This PR fixes the issue, and as a result accelerates affected workloads by ~ 14x ``` # SPDX-FileCopyrightText: Copyright 2026 Arm Limited and/or its affiliates <[email protected]> # SPDX-License-Identifier: MIT import ctypes import statistics import time import faiss import numpy as np rng = np.random.default_rng(1234) d = 128 nlist = 4096 nq = 40_000 quantizer = faiss.IndexFlatL2(d) quantizer.add(rng.random((nlist, d), dtype=np.float32)) index = faiss.IndexIVFPQFastScan(quantizer, d, nlist, 128, 4) # index is empty - this only triggeres the course quantizer index.is_trained = True index.nprobe = 64 xq = rng.random((nq, d), dtype=np.float32) openblas = ctypes.CDLL("libopenblas.so.0") openblas.openblas_get_parallel.restype = ctypes.c_int openblas.openblas_get_num_threads.restype = ctypes.c_int print(f"nq={nq:,}, d={d}, nlist={nlist}, nprobe={index.nprobe}") print(f"OpenMP threads={faiss.omp_get_max_threads()}") print( f"OpenBLAS parallel={openblas.openblas_get_parallel()}, " f"threads={openblas.openblas_get_num_threads()}" ) print(f"BLAS threshold={faiss.cvar.distance_compute_blas_threshold}") index.search(xq, 10) times = [] for _ in range(20): start = time.perf_counter() index.search(xq, 10) times.append(time.perf_counter() - start) elapsed = statistics.median(times) print(f"median={elapsed:.6f}s, throughput={nq / elapsed:,.0f} QPS") ``` Pull Request resolved: https://github.com/facebookresearch/faiss/pull/5669 Reviewed By: weixianghong Differential Revision: D124380509 Pulled By: mnorris11 fbshipit-source-id: 16e1521e7aaf7d65c6a4ce8e404a47bdb26c1198