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77 lines
3.4 KiB
77 lines
3.4 KiB
import os
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import csv
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import numpy as np
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import seaborn as sns
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import matplotlib.pyplot as plt
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output_path = "./plots"
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hbm_result = "./evaluation-results/baseline/current-hbm/qdp-xeonmax-hbm-tca2-tcb0-tcj1-tmul16-wl4294967296-cs2097152.csv"
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dram_result = "./evaluation-results/baseline/current-dram/qdp-xeonmax-dram-tca2-tcb0-tcj1-tmul16-wl4294967296-cs2097152.csv"
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prefetch_result = "./evaluation-results/outofcacheallocation/qdp-xeonmax-prefetch-tca2-tcb1-tcj1-tmul16-wl4294967296-cs8388608.csv"
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distprefetch_result = "./evaluation-results/distprefetch/qdp-xeonmax-distprefetch-tca1-tcb1-tcj1-tmul32-wl4294967296-cs8388608.csv"
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tt_name = "rt-ns"
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function_names = [ "scana-run", "scanb-run", "aggrj-run" ]
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fn_nice = [ "Scan A, Filter", "Scan B, Prefetch", "Aggregate, Project + Sum" ]
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def read_timings_from_csv(fname) -> tuple[list[float], list[str]]:
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t = {}
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row_count = 0
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with open(fname, newline='') as csvfile:
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reader = csv.DictReader(csvfile, delimiter=';')
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for row in reader:
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row_count = row_count + 1
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for i in range(len(function_names)):
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t[fn_nice[i]] = t.get(fn_nice[i], 0) + int(row[function_names[i]])
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t = {key: value / (1000 * 1000 * row_count) for key, value in t.items() if value != 0}
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return list(t.values()), list(t.keys())
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def get_data_prefetch_cache_access() -> tuple[list[float], list[str]]:
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total = 0.47
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data = [ 0.01, 0.01, 0.04, 0.42 ]
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data = [ x * 100 / total for x in data ]
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keys = ["Cache::GetCacheNode", "Cache::Access Itself", "dml::hardware_device::submit", "dml::make_mem_move_task (operator new)"]
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return data,keys
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def get_data_prefetch_total() -> tuple[list[float], list[str]]:
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return read_timings_from_csv(prefetch_result)
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def get_data_dram_total() -> tuple[list[float], list[str]]:
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return
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# loops over all possible configuration combinations and calls
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# process_file_to_dataset for them in order to build a dataframe
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# which is then displayed and saved
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def main(data: tuple[list[float], list[str]], fname, unit):
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palette_color = sns.color_palette('mako_r')
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fig, ax = plt.subplots(figsize=(6, 3), subplot_kw=dict(aspect="equal"))
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wedges, texts = ax.pie(data[0], wedgeprops=dict(width=0.5), startangle=-40, colors=palette_color)
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bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
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kw = dict(arrowprops=dict(arrowstyle="-"), bbox=bbox_props, zorder=0, va="center")
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for i, p in enumerate(wedges):
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ang = (p.theta2 - p.theta1)/2. + p.theta1
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y = np.sin(np.deg2rad(ang))
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x = np.cos(np.deg2rad(ang))
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horizontalalignment = {-1: "right", 1: "left"}[int(np.sign(x))]
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connectionstyle = f"angle,angleA=0,angleB={ang}"
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kw["arrowprops"].update({"connectionstyle": connectionstyle})
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ax.annotate(f"{data[1][i]} - {data[0][i]:2.2f} {unit}", xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y), horizontalalignment=horizontalalignment, **kw)
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fig.savefig(os.path.join(output_path, fname), bbox_inches='tight')
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if __name__ == "__main__":
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main(get_data_prefetch_cache_access(), "plot-timing-cacheaccess.pdf", "%")
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main(read_timings_from_csv(prefetch_result), "plot-timing-prefetch.pdf", "ms")
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main(read_timings_from_csv(distprefetch_result), "plot-timing-distprefetch.pdf", "ms")
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main(read_timings_from_csv(dram_result), "plot-timing-dram.pdf", "ms")
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main(read_timings_from_csv(hbm_result), "plot-timing-hbm.pdf", "ms")
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