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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import os
import json
from typing import List
from numpy import float64
# calculates throughput in gib/s from the meassured
# transfer duration (in nanoseconds) for a given element
# with the size of this given in bytes
def calc_throughput(size_bytes,time_ns):
time_seconds = time_ns * 1e-9
size_gib = size_bytes / (1024 ** 3)
throughput_gibs = size_gib / time_seconds
return throughput_gibs
# reverse array search: return index of value in array
def index_from_element(value,array):
for (idx,val) in enumerate(array):
if val == value: return idx
return 0
# loads the measurements from a given file
def load_time_mesurements(file_path):
with open(file_path, 'r') as file:
data = json.load(file)
count = data["count"]
runcount_divisor = data["list"][0]["task"]["reps"]
# if theres more than one thread, the internal repetition
# count should be the same. if you decide it shouldnt
# remove the check below
if count > 1:
for i in range(count):
if runcount_divisor != data["list"][i]["task"]["reps"]:
print("Runcount missmatch between tasks. Check the commend above, aborting for now.")
os.abort()
return [ x / runcount_divisor for x in data["timings"]]
def get_task_count(file_path):
with open(file_path, 'r') as file:
return json.load(file)["count"]