ayo
7c5d3c9157
Add native K/M/RR inference engines, HIP kernels, replay and validation harnesses, and Windows DLSS setup and runtime support. Include GPU profiling tools, build and packaging scripts, platform integration patches, and implementation documentation.
14 lines
909 B
Python
14 lines
909 B
Python
#!/usr/bin/env python3
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# usage: kprof.py RUN.log FRAMES [WARM] : per-kernel median GPU ms and per-frame total from KERNEL_PROFILE lines
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import sys,re,statistics as st
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lines=[l for l in open(sys.argv[1],errors='replace') if 'KERNEL_PROFILE' in l]
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frames=int(sys.argv[2]); warm=int(sys.argv[3]) if len(sys.argv)>3 else frames//2
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rows=[(int(m.group(1)),float(m.group(2)),m.group(3)) for m in (re.search(r'launch=(\d+).*gpu_ms=([0-9.]+) name=(\S+)',l) for l in lines) if m]
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rows.sort(); n=len(rows); per=n/frames; cut=int(per*warm); rows=rows[cut:]; f=frames-warm
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agg={}
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for _,ms,name in rows: agg.setdefault(name,[]).append(ms)
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tot=sum(sum(v) for v in agg.values())/f
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print(f"launches/frame={per:.1f} total_gpu_ms/frame={tot:.3f} (frames {warm+1}-{frames})")
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for name,v in sorted(agg.items(),key=lambda kv:-sum(kv[1])):
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print(f" {sum(v)/f:8.3f} ms/frame {len(v)/f:5.1f}x med {st.median(v):7.3f} {name}")
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