218 lines
8.4 KiB
Python
218 lines
8.4 KiB
Python
import argparse
|
|
import matplotlib as mpl
|
|
mpl.use('TkAgg')
|
|
import matplotlib.gridspec as gridspec
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
import json
|
|
import glob
|
|
from scipy.integrate import simps
|
|
|
|
|
|
kernel_name_map = {
|
|
"unopt": "reference FPGA kernel",
|
|
"memory": "memory optimized kernel",
|
|
"ndrange": "NDRange optimized kernel",
|
|
"final": "fully optimized kernel"
|
|
}
|
|
cmap = plt.get_cmap('cividis')
|
|
colors = [cmap(i) for i in np.linspace(0, 1, 6)]
|
|
|
|
|
|
def main(corpora, sizes, lengths, fpgadir, cpudir, save, count, kernel):
|
|
plt.style.use('seaborn')
|
|
|
|
fpgaresults = parse_fpga_results(corpora, sizes, lengths, fpgadir)
|
|
cpuresults = parse_cpu_results(corpora, sizes, lengths, cpudir)
|
|
plot_speedup(fpgaresults, cpuresults, corpora, sizes, lengths, save, count, kernel)
|
|
plot_speedup_cycles(fpgaresults, cpuresults, corpora, sizes, lengths, save, kernel)
|
|
print_energy_table(fpgaresults, cpuresults, corpora, sizes, lengths)
|
|
|
|
|
|
def parse_fpga_results(corpora, sizes, lengths, dir):
|
|
results = dict()
|
|
|
|
for corpus in corpora:
|
|
results[corpus] = dict()
|
|
for size in sizes:
|
|
results[corpus][size] = dict()
|
|
for length in lengths:
|
|
results[corpus][size][length] = parse_fpga_result(corpus, size, length, dir)
|
|
|
|
return results
|
|
|
|
|
|
def parse_fpga_result(corpus, size, length, dir):
|
|
resultdir = f"{dir}/{corpus}.{size}MB.len{length}"
|
|
result = []
|
|
for filepath in glob.iglob(f"{resultdir}/*"):
|
|
with open(filepath, "r") as f:
|
|
data = json.load(f)
|
|
time = data['Kernel Execution'][0]['time']
|
|
energy = get_energy_usage(data)
|
|
result.append((time/1000, energy))
|
|
|
|
return result
|
|
|
|
|
|
def get_energy_usage(data):
|
|
xs = list(map(lambda x: x["timestamp"], data["power"]))
|
|
ys = list(map(lambda y: y["power"], data["power"]))
|
|
|
|
# Get start and end timestamp of kernel execution.
|
|
start = float(data["timeline"]["START"])
|
|
end = float(data["timeline"]["END"])
|
|
|
|
# Find nearest power data points.
|
|
nearest_start = min(range(len(xs)), key=lambda i: abs(xs[i] - start))
|
|
nearest_end = min(range(len(xs)), key=lambda i: abs(xs[i] - end))
|
|
|
|
# Find power data points within kernel execution
|
|
kernel_xs = np.array(xs)[nearest_start:nearest_end+1] / 1000
|
|
kernel_ys = np.array(ys)[nearest_start:nearest_end+1]
|
|
|
|
# Use Simpson's Rule to integrate and find energy usage.
|
|
return simps(y=kernel_ys, x=kernel_xs)
|
|
|
|
|
|
def parse_cpu_results(corpora, sizes, lengths, dir):
|
|
results = dict()
|
|
|
|
for corpus in corpora:
|
|
results[corpus] = dict()
|
|
for size in sizes:
|
|
results[corpus][size] = dict()
|
|
for length in lengths:
|
|
results[corpus][size][length] = parse_cpu_result(corpus, size, length, dir)
|
|
|
|
return results
|
|
|
|
|
|
def parse_cpu_result(corpus, size, length, dir):
|
|
def parse_line(line):
|
|
[range_time, index_time, total_matches] = line.split(" ")
|
|
return (float.fromhex(range_time), float.fromhex(index_time), int(total_matches))
|
|
|
|
filename = f"{dir}/{corpus}.{size}MB.cpu{length}.result"
|
|
|
|
with open(filename, "r") as f:
|
|
data = list(map(parse_line, f.read().splitlines()))
|
|
|
|
return data
|
|
|
|
|
|
def plot_speedup(fpgaresults, cpuresults, corpora, sizes, lengths, save, count, kernel):
|
|
def calculate_speedup(corpus, size, length):
|
|
result = []
|
|
for (fpgaresult, cpuresult) in zip(fpgaresults[corpus][size][length], cpuresults[corpus][size][length]):
|
|
fpgatime = count / fpgaresult[0]
|
|
cputime = count / cpuresult[0]
|
|
result.append(cputime / fpgatime)
|
|
return result
|
|
|
|
width = .1
|
|
labels = [f"{size}MB" for size in sizes]
|
|
xs = np.arange(len(labels))
|
|
gs = gridspec.GridSpec(2, 4)
|
|
gs.update(wspace=1.0, hspace=0.5)
|
|
|
|
axes = [plt.subplot(gs[0, 1:3], ), plt.subplot(gs[1, :2]), plt.subplot(gs[1, 2:])]
|
|
for axi, ax in enumerate(axes):
|
|
corpus = corpora[axi]
|
|
ax.sharey(axes[0])
|
|
|
|
means = np.array([[np.mean(calculate_speedup(corpus, size, length)) for size in sizes] for length in lengths])
|
|
stds = np.array([[np.std(calculate_speedup(corpus, size, length)) for size in sizes] for length in lengths])
|
|
for i in range(len(lengths)):
|
|
ax.bar(xs - (width*len(lengths))/2 + i * width + width/2, means[i], width, label=f"{lengths[i]} characters", yerr=stds[i], color=colors[i])
|
|
|
|
if axi in [0, 1]:
|
|
ax.set_ylabel("Throughput CPU / FPGA")
|
|
ax.set_xlabel("Corpus size (MB)")
|
|
ax.set_title(f"\"{corpus}\" corpus");
|
|
ax.set_xticks(xs)
|
|
ax.set_xticklabels(labels)
|
|
if axi == 0:
|
|
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0., title="Pattern length")
|
|
|
|
plt.suptitle(f"Throughput comparison of the {kernel_name_map[kernel]} and reference CPU application")
|
|
|
|
if save:
|
|
figure = plt.gcf()
|
|
figure.set_size_inches(9, 7)
|
|
plt.savefig(f"speedup_{kernel}.png", format="png", dpi=100)
|
|
else:
|
|
plt.show()
|
|
|
|
|
|
def plot_speedup_cycles(fpgaresults, cpuresults, corpora, sizes, lengths, save, kernel):
|
|
fpga_clockspeed = 0.3
|
|
cpu_clockspeed = 3.4
|
|
def calculate_speedup(corpus, size, length):
|
|
result = []
|
|
for (fpgaresult, cpuresult) in zip(fpgaresults[corpus][size][length], cpuresults[corpus][size][length]):
|
|
fpgacycles = fpgaresult[0] * fpga_clockspeed
|
|
cpucycles = cpuresult[0] * cpu_clockspeed
|
|
result.append(cpucycles / fpgacycles)
|
|
return result
|
|
|
|
width = .1
|
|
labels = [f"{size}MB" for size in sizes]
|
|
xs = np.arange(len(labels))
|
|
gs = gridspec.GridSpec(2, 4)
|
|
gs.update(wspace=1.0, hspace=0.5)
|
|
|
|
axes = [plt.subplot(gs[0, 1:3], ), plt.subplot(gs[1, :2]), plt.subplot(gs[1, 2:])]
|
|
for axi, ax in enumerate(axes):
|
|
corpus = corpora[axi]
|
|
ax.sharey(axes[0])
|
|
|
|
means = np.array([[np.mean(calculate_speedup(corpus, size, length)) for size in sizes] for length in lengths])
|
|
stds = np.array([[np.std(calculate_speedup(corpus, size, length)) for size in sizes] for length in lengths])
|
|
for i in range(len(lengths)):
|
|
ax.bar(xs - (width*len(lengths))/2 + i * width + width/2, means[i], width, label=f"{lengths[i]} characters", yerr=stds[i], color=colors[i+3])
|
|
|
|
if axi in [0, 1]:
|
|
ax.set_ylabel("Cycles CPU / FPGA")
|
|
ax.set_xlabel("Corpus size (MB)")
|
|
ax.set_title(f"\"{corpus}\" corpus");
|
|
ax.set_xticks(xs)
|
|
ax.set_xticklabels(labels)
|
|
if axi == 0:
|
|
ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0., title="Pattern length")
|
|
|
|
plt.suptitle(f"Clock cycle comparison of the {kernel_name_map[kernel]} and the reference CPU application")
|
|
|
|
if save:
|
|
figure = plt.gcf()
|
|
figure.set_size_inches(9, 7)
|
|
plt.savefig(f"speedup_cycles_{kernel}.png", format="png", dpi=100)
|
|
else:
|
|
plt.show()
|
|
|
|
|
|
def print_energy_table(fpgaresults, cpuresults, corpora, sizes, lengths):
|
|
for length in lengths:
|
|
print(f"{length}", end="")
|
|
for corpus in corpora:
|
|
for size in sizes:
|
|
cpumean = np.mean([result[1] for result in cpuresults[corpus][size][length]])
|
|
fpgamean = np.mean([result[1] for result in fpgaresults[corpus][size][length]])
|
|
comp = fpgamean / (cpumean / 1000000)
|
|
print(" & ${:.1f}\\times$".format(comp), end="")
|
|
print(" \\\\")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("-c", "--count", help="number of patterns", type=int, required=True)
|
|
parser.add_argument("-l", "--lengths", help="length of the patterns", type=int, nargs="+", default=[], required=True)
|
|
parser.add_argument("-f", "--fpgadir", help="directory containing FPGA results", required=True)
|
|
parser.add_argument("-d", "--cpudir", help="directory containing CPU results", required=True)
|
|
parser.add_argument("-t", "--corpora", help="text corpora (without file size)", nargs="+", default=[], required=True)
|
|
parser.add_argument("-s", "--sizes", help="file sizes", type=int, nargs="+", default=[], required=True)
|
|
parser.add_argument("-k", "--kernel", help="kernel", required=True)
|
|
parser.add_argument("-o", "--save", help="save as PNG", action="store_true", required=False)
|
|
args = parser.parse_args()
|
|
|
|
main(args.corpora, args.sizes, args.lengths, args.fpgadir, args.cpudir, args.save, args.count, args.kernel)
|