This contains my bachelors thesis and associated tex files, code snippets and maybe more. Topic: Data Movement in Heterogeneous Memories with Intel Data Streaming Accelerator
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  1. import os
  2. import json
  3. import pandas as pd
  4. from itertools import chain
  5. import seaborn as sns
  6. import matplotlib.pyplot as plt
  7. runid = "Run ID"
  8. x_label = "Thread Count"
  9. y_label = "Throughput in GiB/s LogScale"
  10. var_label = "Thread Counts"
  11. thread_counts = ["1t", "2t", "4t", "8t", "12t"]
  12. thread_counts_nice = ["1 Thread", "2 Threads", "4 Threads", "8 Threads", "12 Threads"]
  13. engine_counts = ["1e", "4e"]
  14. engine_counts_nice = ["1 E/WQ", "4 E/WQ"]
  15. title = "Per-Thread Throughput - 120 Copy Operations split on Threads Intra-Node on DDR with Size 1 MiB"
  16. index = [runid, x_label, var_label]
  17. data = []
  18. def calc_throughput(size_bytes,time_microseconds):
  19. time_seconds = time_microseconds * 1e-9
  20. size_gib = size_bytes / (1024 ** 3)
  21. throughput_gibs = size_gib / time_seconds
  22. return throughput_gibs
  23. def index_from_element(value,array):
  24. for (idx,val) in enumerate(array):
  25. if val == value: return idx
  26. return 0
  27. def load_and_process_copy_json(file_path,mode):
  28. with open(file_path, 'r') as file:
  29. data = json.load(file)
  30. count = data["count"]
  31. return {
  32. "combined" : [x / ((120 / count) * count) for x in list(chain(*[data["list"][i]["report"]["time"]["combined"] for i in range(count)]))],
  33. "submission" : [x / ((120 / count) * count) for x in list(chain(*[data["list"][i]["report"]["time"]["submission"] for i in range(count)]))],
  34. "completion" : [x / ((120 / count) * count) for x in list(chain(*[data["list"][i]["report"]["time"]["completion"] for i in range(count)]))]
  35. }
  36. # Function to plot the graph for the new benchmark
  37. def create_mtsubmit_dataset(file_paths, engine_label):
  38. times = []
  39. engine_index = index_from_element(engine_label,engine_counts)
  40. engine_nice = engine_counts_nice[engine_index]
  41. idx = 0
  42. for file_path in file_paths:
  43. time = load_and_process_copy_json(file_path, engine_label)
  44. times.append(time["combined"])
  45. idx = idx + 1
  46. throughput = [[calc_throughput(1024*1024,time) for time in t] for t in times]
  47. idx = 0
  48. for run_set in throughput:
  49. run_idx = 0
  50. for run in run_set:
  51. data.append({ runid : run_idx, x_label: thread_counts_nice[idx], var_label : engine_nice, y_label : throughput[idx][run_idx]})
  52. run_idx = run_idx + 1
  53. idx = idx + 1
  54. # Main function to iterate over files and create plots for the new benchmark
  55. def main():
  56. folder_path = "benchmark-results/" # Replace with the actual path to your folder
  57. for engine_label in engine_counts:
  58. mt_file_paths = [os.path.join(folder_path, f"mtsubmit-{thread_count}-{engine_label}.json") for thread_count in thread_counts]
  59. create_mtsubmit_dataset(mt_file_paths, engine_label)
  60. df = pd.DataFrame(data)
  61. df.set_index(index, inplace=True)
  62. sns.barplot(x=x_label, y=y_label, hue=var_label, data=df, palette="rocket", errorbar="sd").set(yscale="log")
  63. plt.title(title)
  64. plt.savefig(os.path.join(folder_path, "plot-perf-mtsubmit.png"), bbox_inches='tight')
  65. plt.show()
  66. if __name__ == "__main__":
  67. main()