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re-plot the benchmarks with the new data

master
Constantin Fürst 1 year ago
parent
commit
c0f2aa2b64
  1. 10
      benchmarks/benchmark-plotters/plot-perf-peakthroughput.py
  2. BIN
      benchmarks/benchmark-results/plot-perf-allnodethroughput.png
  3. BIN
      benchmarks/benchmark-results/plot-perf-peakthroughput.png
  4. BIN
      benchmarks/benchmark-results/plot-perf-smartthroughput.png

10
benchmarks/benchmark-plotters/plot-perf-peakthroughput.py

@ -11,7 +11,7 @@ runid = "Run ID"
x_label = "Destination Node" x_label = "Destination Node"
y_label = "Source Node" y_label = "Source Node"
v_label = "Throughput" v_label = "Throughput"
title = "Copy Throughput for 1GiB Elements running on SRC Node"
title = "Copy Throughput in GiB/s tested for 1GiB Elements using all 8 DSA Chiplets"
index = [ runid, x_label, y_label] index = [ runid, x_label, y_label]
data = [] data = []
@ -34,11 +34,11 @@ def load_time_mesurements(file_path):
with open(file_path, 'r') as file: with open(file_path, 'r') as file:
data = json.load(file) data = json.load(file)
count = data["count"] count = data["count"]
batch_size = 8
batch_size = data["list"][0]["task"]["batching"]["batch_size"] if data["list"][0]["task"]["batching"]["batch_size"] > 0 else 1
iterations = data["list"][0]["task"]["iterations"] iterations = data["list"][0]["task"]["iterations"]
return { return {
"total": sum([x / (iterations * batch_size * count) for x in list(chain([data["list"][i]["report"]["time"]["total"] for i in range(count)]))]),
"total": sum([x / (iterations * batch_size * count * count) for x in list(chain([data["list"][i]["report"]["time"]["total"] for i in range(count)]))]),
"combined": [ x / (count * batch_size) for x in list(chain(*[data["list"][i]["report"]["time"]["combined"] for i in range(count)]))], "combined": [ x / (count * batch_size) for x in list(chain(*[data["list"][i]["report"]["time"]["combined"] for i in range(count)]))],
"submission": [ x / (count * batch_size) for x in list(chain(*[data["list"][i]["report"]["time"]["submission"] for i in range(count)]))], "submission": [ x / (count * batch_size) for x in list(chain(*[data["list"][i]["report"]["time"]["submission"] for i in range(count)]))],
"completion": [ x / (count * batch_size) for x in list(chain(*[data["list"][i]["report"]["time"]["completion"] for i in range(count)]))] "completion": [ x / (count * batch_size) for x in list(chain(*[data["list"][i]["report"]["time"]["completion"] for i in range(count)]))]
@ -63,7 +63,7 @@ def main():
for src_node in range(16): for src_node in range(16):
for dst_node in range(16): for dst_node in range(16):
file = os.path .join(folder_path, f"copy-n{src_node}ton{dst_node}-1gib-4e.json")
file = os.path.join(folder_path, f"copy-n{src_node}ton{dst_node}-1gib-allnodes-1e.json")
process_file_to_dataset(file, src_node, dst_node) process_file_to_dataset(file, src_node, dst_node)
df = pd.DataFrame(data) df = pd.DataFrame(data)
@ -73,7 +73,7 @@ def main():
sns.heatmap(data_pivot, annot=True, cmap="YlGn", fmt=".0f") sns.heatmap(data_pivot, annot=True, cmap="YlGn", fmt=".0f")
plt.title(title) plt.title(title)
plt.savefig(os.path.join(folder_path, "plot-perf-peakthroughput.png"), bbox_inches='tight')
plt.savefig(os.path.join(folder_path, "plot-perf-allnodethroughput.png"), bbox_inches='tight')
plt.show() plt.show()

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benchmarks/benchmark-results/plot-perf-allnodethroughput.png

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benchmarks/benchmark-results/plot-perf-peakthroughput.png

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benchmarks/benchmark-results/plot-perf-smartthroughput.png

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