108 lines
2.8 KiB
Python
108 lines
2.8 KiB
Python
from pathlib import Path
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import json
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from dataclasses import dataclass
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import numpy as np
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import matplotlib.pyplot as plt
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RESULTS_DIR = Path(__file__).parent
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## Read all data
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data = {}
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for e in RESULTS_DIR.iterdir():
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if e.suffix != ".json":
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continue
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print(f'load {e}')
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with open(e, 'r') as f:
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try:
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res = json.load(f)
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except json.decoder.JSONDecodeError as e:
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print(f"SKIP - incorrect formatting")
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continue
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benchmarks = res["benchmarks"]
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for benchmark in benchmarks:
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run_type = benchmark["run_type"]
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if run_type == "iteration":
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run_name = benchmark["run_name"]
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if "Comm_prefetch_managed_GPUToGPU/0/0" in run_name:
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continue
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elif "/0/0/" in run_name:
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continue
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xs, ys = data.get(run_name, ([],[]))
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ys += [benchmark["real_time"]]
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xs += [int(benchmark["bytes"])]
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data[run_name] = (xs, ys)
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## compute aggregates
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for name, (xs, ys) in data.items():
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assert all(xs[0] == x_i for x_i in xs)
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b = xs[0] # bytes
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times = ys
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bws = [b / y for y in ys]
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times_mean = np.mean(times)
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times_stddev = np.std(times)
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bws_mean = np.mean(bws)
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bws_stddev = np.std(bws)
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data[name] = (b, times_mean, times_stddev, bws_mean, bws_stddev)
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## split data by name
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series = {}
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for name, point in data.items():
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name, f1, f2 = name.split("/")[0:3]
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# expect these to be ints
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f1 = int(f1)
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f2 = int(f2)
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name = "/".join((name, str(f1), str(f2)))
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s = series.get(name, [])
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s += [point]
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series[name] = s
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# sort all series
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for name, points in series.items():
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series[name] = sorted(points, key=lambda p: p[0])
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# split to x,t, terr, bw, bwerr
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for name, points in series.items():
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# [(x,y,z), ...] -> ([x...], [y...], [z...])
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x, t, terr, bw, bwerr = zip(*points)
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series[name] = (x, t, terr, bw, bwerr)
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# print(series)
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for name, (x, t, terr, bw, bwerr) in series.items():
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plt.errorbar(x, bw, yerr=bwerr, label=name)
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for pattern in [
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"hipManaged_HostToGPUWriteDst",
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"hipMemcpyAsync_GPUToGPU",
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"hipMemcpyAsync_GPUToPageable",
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"hipMemcpyAsync_GPUToPinned",
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"implicit_managed_GPURdHost_coarse",
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"implicit_managed_GPURdHost_fine",
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"implicit_managed_GPUWrGPU_coarse",
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"implicit_managed_GPUWrGPU_fine",
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"implicit_mapped_GPURdHost",
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"prefetch_managed_GPUToGPU",
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"prefetch_managed_GPUToHost",
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]:
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plt.clf()
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for name, (x, t, terr, bw, bwerr) in series.items():
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if pattern not in name:
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continue
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plt.errorbar(x, bw, yerr=bwerr, label=name)
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output_path = f"{pattern}.pdf"
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print(f"write {output_path}")
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plt.xscale('log')
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lgd = plt.legend(bbox_to_anchor=(1.04, 1))
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plt.tight_layout()
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plt.savefig(output_path, bbox_extra_artists=(lgd,), bbox_inches='tight') |