优化绑核逻辑,取消硬编码改为智能识别

This commit is contained in:
CGH0S7
2026-02-25 10:59:32 +08:00
parent 82339f5282
commit e2bc472845

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@@ -11,17 +11,46 @@
import AMSS_NCKU_Input as input_data import AMSS_NCKU_Input as input_data
import subprocess import subprocess
import time import time
## CPU core binding configuration using taskset
## taskset ensures all child processes inherit the CPU affinity mask
## This forces make and all compiler processes to use only nohz_full cores (4-55, 60-111)
## Format: taskset -c 4-55,60-111 ensures processes only run on these cores
#NUMACTL_CPU_BIND = "taskset -c 0-111"
NUMACTL_CPU_BIND = "taskset -c 16-47,64-95"
## Build parallelism configuration
## Use nohz_full cores (4-55, 60-111) for compilation: 52 + 52 = 104 cores def get_last_n_cores_per_socket(n=32):
## Set make -j to utilize available cores for faster builds """
BUILD_JOBS = 96 Read CPU topology via lscpu and return a taskset -c string
selecting the last `n` cores of each NUMA node (socket).
Example: 2 sockets x 56 cores each, n=32 -> node0: 24-55, node1: 80-111
-> "taskset -c 24-55,80-111"
"""
result = subprocess.run(["lscpu", "--parse=NODE,CPU"], capture_output=True, text=True)
# Build a dict: node_id -> sorted list of CPU ids
node_cpus = {}
for line in result.stdout.splitlines():
if line.startswith("#") or not line.strip():
continue
parts = line.split(",")
if len(parts) < 2:
continue
node_id, cpu_id = int(parts[0]), int(parts[1])
node_cpus.setdefault(node_id, []).append(cpu_id)
segments = []
for node_id in sorted(node_cpus):
cpus = sorted(node_cpus[node_id])
selected = cpus[-n:] # last n cores of this socket
segments.append(f"{selected[0]}-{selected[-1]}")
cpu_str = ",".join(segments)
total = len(segments) * n
print(f" CPU binding: taskset -c {cpu_str} ({total} cores, last {n} per socket)")
return f"taskset -c {cpu_str}"
## CPU core binding: dynamically select the last 32 cores of each socket (64 cores total)
NUMACTL_CPU_BIND = get_last_n_cores_per_socket(n=32)
## Build parallelism: match the number of bound cores
BUILD_JOBS = 64
################################################################## ##################################################################