import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots

pd.set_option('display.max_columns', None)
pd.set_option('display.float_format', lambda x: '%.5f' % x)

Jetson Nano Measurements¶

Measurements are done using the tegrastats tool and a custom wrapper which converts to csv output.

def read_csv(path):
    df = pd.read_csv(path)
    df["dt"] = df["TIME"] - df["TIME"][0] - 10
    
    return df
df = read_csv("jetson-nano-clients_10-minpower-vhf.csv")
df

Measurements: Dynamic addition of clients (1, 5 and 10 clients) at custom low power (1000 hz sampling rate)¶

def plot_power2(df):
    fig = make_subplots(specs=[[{"secondary_y": True}]])
    fig.layout.margin = go.layout.Margin(l=10, r=10, b=10, t=10)

    fig.add_trace(go.Scatter(x=df["dt"], y=df["POM_5V_CPU_CUR"].rolling(200).mean(), name="CPU Power"))
    fig.add_trace(go.Scatter(x=df["dt"], y=df["POM_5V_GPU_CUR"].rolling(200).mean(), name="GPU Power"))
    fig.add_trace(go.Scatter(x=df["dt"], y=df["POM_5V_IN_CUR"].rolling(200).mean(), name="Total Power"))

    #fig.add_trace(go.Scatter(x=df["dt"], y=df["CPU Usage"].rolling(10).mean(), name="CPU Usage"), secondary_y=True,)
    #fig.add_trace(go.Scatter(x=df["dt"], y=df["GR3D_FREQ_UTIL"].rolling(50).mean(), name="GPU Usage"), secondary_y=True,)

    fig.add_trace(go.Scatter(x=df["dt"], y=df["GR3D_FREQ_UTIL"].rolling(500).mean(), name="GPU Utilization",
                            line = dict(color='#FFA15A', dash='dot')), secondary_y=True,)
    fig.add_trace(go.Scatter(x=df["dt"], y=(df["CPU_0"]+df["CPU_1"]).rolling(500).mean(), name="CPU Utilization",
                            line = dict(color='#19D3F3', dash='dot')), secondary_y=True,)


    fig.update_xaxes(title_text="Time (s)")
    fig.update_yaxes(title_text="Power Consumption (mW)")
    fig.update_yaxes(title_text="Utilization (%)", secondary_y=True)
    
    return fig

fig = plot_power2(df)
fig.add_vline(x=7, annotation_text="Start")
fig.add_vline(x=36, annotation_text="Pipeline Running")
fig.add_vline(x=120, annotation_text="Power Client 2-5")
fig.add_vline(x=180, annotation_text="Power Client 6-10")

fig.show("notebook")

	TIME	RAM	SWAP	IRAM	CPU_0	CPU_1	CPU_2	CPU_3	EMC_FREQ	GR3D_FREQ_UTIL	GR3D_FREQ_FREQ	PLL	CPU	PMIC	GPU	AO	thermal	APE	POM_5V_IN_CUR	POM_5V_IN_AVG	POM_5V_GPU_CUR	POM_5V_GPU_AVG	POM_5V_CPU_CUR	POM_5V_CPU_AVG	dt
0	1648029257.71422	749	0	0	50	0	off	off	0	0	76	39.00000	43.00000	50	43.00000	45.00000	43.00000	25	2111	2111	0	0	414	414	-10.00000
1	1648029257.72400	749	0	0	0	0	off	off	0	0	76	39.00000	43.00000	50	43.00000	45.00000	43.00000	25	2111	2111	0	0	414	414	-9.99022
2	1648029257.73260	749	0	0	0	0	off	off	0	0	76	39.00000	43.00000	50	43.00000	45.00000	43.00000	25	2111	2111	0	0	414	414	-9.98162
3	1648029257.74147	749	0	0	100	0	off	off	0	0	76	39.00000	43.00000	50	43.00000	45.00000	43.00000	25	2153	2121	0	0	455	424	-9.97276
4	1648029257.75011	749	0	0	100	0	off	off	0	0	76	39.00000	43.00000	50	43.00000	45.00000	43.00000	25	2153	2127	0	0	455	430	-9.96411
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
27509	1648029553.73134	2290	0	0	50	50	off	off	8	99	230	40.50000	45.00000	50	42.50000	46.50000	43.75000	25	3281	2934	697	540	492	482	286.01712
27510	1648029553.74219	2290	0	0	0	0	off	off	8	99	230	40.50000	45.50000	50	42.50000	46.50000	43.75000	25	3281	2934	738	540	492	482	286.02797
27511	1648029553.75366	2290	0	0	100	0	off	off	8	99	230	40.50000	45.00000	50	42.00000	46.50000	43.75000	25	3281	2934	738	540	492	482	286.03944
27512	1648029553.76484	2290	0	0	100	0	off	off	8	99	230	40.50000	45.00000	50	42.00000	46.50000	43.75000	25	3281	2934	779	540	451	482	286.05062
27513	1648029553.77727	2290	0	0	100	0	off	off	8	99	230	40.50000	45.00000	50	42.00000	46.50000	43.75000	25	3317	2934	779	540	450	482	286.06305