I have 2D array C which I instantiate like this:
const int wA = 16;
float * C[wA];
for(int i = 0; i < hA; i++)
{
C[i] = new float[hA];
for(int i2 = 0; i2 < hA; i2++)
C[i][i2] = 0;
}
/* looks like this:
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
*/
I create a kernel which operates on C:
__kernel void simpleMultiply(__global float* outputC,
int widthA,
int heightA,
int widthB,
int heightB,
__global float * inputA,
__global float * inputB)
{
int row = get_global_id(1);
int col = get_global_id(0);
float sum = 0.0f;
for(int i = 0; i < widthA; i++)
{
sum += inputA[row*widthA+i] * inputB[i*widthB+col];
}
outputC[row*widthB+col] = sum;
}
and everything goes fine. I get CL_SUCCESS for status all the way through from setting up the context to creating the buffers, creating the kernel, program, clEnqueueNDRangeKernel, clEnqueueReadBuffer, etc.
But when I go to read the output it crashes.
status = clEnqueueNDRangeKernel(cmdQueue, kernel, 2, NULL, globalws, localws, 0, NULL, NULL);
cout << "\nclEnqueueNDRangeKernel: " << (status == CL_SUCCESS ? "SUCCESS" : "FAIL"); // prints SUCCESS
status = clEnqueueReadBuffer(cmdQueue, bufferC, CL_TRUE, 0, wC*hC*sizeof(float), (void*)C, 0, NULL, NULL);
cout << "\nclEnqueueReadBuffer: " << (status == CL_SUCCESS ? "SUCCESS" : "FAIL"); // prints SUCCESS
cout << "\nC[0][0]: " << C[0][0]; // <--crash
I am just as new to C++ as I am OpenCL so this could be due to a weak understanding of arrays and pointers in c++.
The whole code is here
The array
float * C[wA];
Is a 1D array of float * pointers. So you haven't created, in memory, a 2D array with contiguous rows and columns. But you've created an array of pointers to rows.
So you should flatten on the host array C and index into it the same way as you do on the kernel.
float * C;
c = new float[ha * ha]; // Create a contiguous memory area to be addressed in a 2D pattern
memset( C, 0, ha * ha * sizeof(float) ); // Set all bytes to zero
...
Now you can address the C array after running the kernel
cout << C[ icolumn + irow * ha ]; // icolumn and irow are your row and columns indices
Related
I have a geotiff image created using GDAL.
I would like to know if it is possible using GDAL and Python (or only one of them) to extract the pixel percentage of specific value.
In particular, if I do:
gdalinfo -hist input.tif
I get all the metadata info and, in particular,
Size is 4901, 2867
...
Band 1 Block=4901x1 Type=Byte, ColorInterp=Palette
Minimum=0.000, Maximum=5.000, Mean=2.263, StdDev=1.135
0...10...20...30...40...50...60...70...80...90...100 - done.
256 buckets from -0.5 to 255.5:
1740973 365790 6385650 3688110 1757506 113138 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Is there a way to calculate the pixel percentage of the 6 values defined in the histogram?
The size of the .tif file is 4901x2867 so if I can extract each of those fields using GDAL and/or Python then I can calculate something like this:
pixel_value_0 = 1740973/(4901x2867)
and get the percentage of the pixel value 0
You can convert raster image to a Numpy array and then do the calculation if using Python
from collections import Counter
from osgeo import gdal_array
# Read raster data as numeric array from file
rasterArray = gdal_array.LoadFile('RGB.byte.tif')
# The 3rd band
band3 = rasterArray[2]
# Flatten the 2D array to 1D and count occurrences of each values
# Then simple to get the stat for a pixel value in particular
print(Counter(band3.flatten()))
I am trying to make function that returns geometric mean with data.
I want to make loop with try() to pass valid data, but what i tried actually didn't worked.
This is function
R=function(g)
{
k=1
n=length(g)
for(i in 1 : n)
{
ifelse(g[i]>0, k<-k*g[i], stop("Negative component"))
k
}
t=k^(1/n)
t
}
And i want to use this function in this loop
set.seed(123)
data <- matrix(rnorm(10000, mean=3), ncol=25, dimnames=list(NULL, paste("X",
1:25, sep=".")))
v=rep(0,400)
for(i in 1 : 400)
{
try("v[i]=R(data[,i])",TRUE)
}
v
I want to get means for valid data, but it makes all value to 0
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[37] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[73] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[109] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[145] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[181] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[217] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[253] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[289] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[325] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[361] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[397] 0 0 0 0
Can you let me know where was wrong??
Thanks
I guess the problem is with the missing declaration of t as a numeric vector and the syntax of try() function. The following code should work, it worked for me.
R=function(g)
{
t = numeric(length(g))
k=1
n=length(g)
for(i in 1 : n)
{
ifelse(g[i]>0, k<-k*g[i], stop("Negative component"))
}
t=k^(1/n)
t
}
set.seed(123)
data <- matrix(rnorm(10000, mean=3), ncol=25, dimnames=list(NULL,paste("X",
1:25, sep=".")))
v=numeric(400)
for(i in 1 : 400)
{
try(v[i]<-R(data[i,]),TRUE)
}
v
I am able to follow the Circlize example in the description of the package on CRAN easily:
library('circlize')
set.seed(123)
mat = matrix(sample(1:100, 18, replace = TRUE), 3, 6)
rownames(mat) = letters[1:3]
colnames(mat) = LETTERS[1:6]
### basic settings
par(mfrow = c(3, 2))
par(mar = c(1, 1, 1, 1))
chordDiagram(mat)
however, when I replace mat with myMatrix I get this error:
Error in circos.initialize(factors = factor(cate, levels = cate), xlim = cbind(rep(0, :
Since `xlim` is a matrix, it should have same number of rows as the length of the level of `factors` and number of columns of 2.
Can somebody explain why I am getting that message? I do not see a difference between mat and myMatrix other than myMatrix is larger:
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z A2 B2 C2 D2
A 1060360.659 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
B 0 32143148.75 996976.8445 0 4944648.524 5688385.041 61990.5913 0 0 0 0 -1563.225 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 31922242.6
C 0 0 6342776.843 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
D 0 0 0 28617385.81 17842142.64 0 0 0 0 0 0 0 0 409444.5633 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
E 0 0 0 4990921.202 105686446.3 536246.2188 0 0 0 0 0 0 0 8587899.583 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 378565.5746
F 0 92732.7741 0 4282.9319 33543553.89 36773976.59 1894761.93 0 0 333209.342 0 20739.0655 327956.7365 0 1022673.163 12229.0255 0 0 386112.1743 224039.3207 0 2395066.197 268247.2897 0 0 0 0 0 0 11926701.96
G 0 0 0 0 0 0 7753767.003 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
H 0 0 0 0 0 5184133.29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I 0 0 0 0 462767.7374 0 0 0 8992223.296 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
J 0 0 0 0 0 0 0 0 0 1950552.642 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
K 0 0 0 0 891032.5584 0 0 0 0 0 520107.9821 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26724.8402 0 0 0 418902.5203
L 0 0 0 0 32044317.54 28147.5693 0 0 0 0 0 5383919.293 0 489912.5412 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4559115.003
M 0 0 0 0 0 3125823.41 0 0 0 0 0 0 1738293.164 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
N 0 1053825.966 -8526.9758 1283429.314 60333051.34 2621812.931 -1130.1924 0 -779545.8004 8055145.684 918.8702 -379747.1919 -177.6205 298563606.5 -9316.8654 0 0 0 0 0 2631991.077 0 0 0 0 0 1107369.803 0 0 118812465
O 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1500451.292 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7432418.396
P 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Q 0 0 1496058.76 0 -4056617.74 294503 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 410.4 0 0 0 0 0 0 0 1765984767
Code
dd <- read.table(header = TRUE, text = " rn A B C D E F G H I J K L M N O P Q R S T U V W X Y Z A2 B2 C2 D2
A 1060360.659 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
B 0 32143148.75 996976.8445 0 4944648.524 5688385.041 61990.5913 0 0 0 0 -1563.225 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 31922242.6
C 0 0 6342776.843 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
D 0 0 0 28617385.81 17842142.64 0 0 0 0 0 0 0 0 409444.5633 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
E 0 0 0 4990921.202 105686446.3 536246.2188 0 0 0 0 0 0 0 8587899.583 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 378565.5746
F 0 92732.7741 0 4282.9319 33543553.89 36773976.59 1894761.93 0 0 333209.342 0 20739.0655 327956.7365 0 1022673.163 12229.0255 0 0 386112.1743 224039.3207 0 2395066.197 268247.2897 0 0 0 0 0 0 11926701.96
G 0 0 0 0 0 0 7753767.003 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
H 0 0 0 0 0 5184133.29 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I 0 0 0 0 462767.7374 0 0 0 8992223.296 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
J 0 0 0 0 0 0 0 0 0 1950552.642 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
K 0 0 0 0 891032.5584 0 0 0 0 0 520107.9821 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26724.8402 0 0 0 418902.5203
L 0 0 0 0 32044317.54 28147.5693 0 0 0 0 0 5383919.293 0 489912.5412 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4559115.003
M 0 0 0 0 0 3125823.41 0 0 0 0 0 0 1738293.164 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
N 0 1053825.966 -8526.9758 1283429.314 60333051.34 2621812.931 -1130.1924 0 -779545.8004 8055145.684 918.8702 -379747.1919 -177.6205 298563606.5 -9316.8654 0 0 0 0 0 2631991.077 0 0 0 0 0 1107369.803 0 0 118812465
O 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1500451.292 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7432418.396
P 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Q 0 0 1496058.76 0 -4056617.74 294503 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 410.4 0 0 0 0 0 0 0 1765984767")
myMatrix <- as.matrix(dd[, -1])
rownames(myMatrix) <- dd[, 1]
chordDiagram(myMatrix)
In the old version of circlize, the matrix must be of a matrix class instead of a data.frame, so you need to convert the data frame explicitly by:
myMatrix = as.matrix(A + B)
In circlize, a data frame is for data stored as a adjacency list (e.g the first column for group1, second column for group2, third column for the strength of the relation).
Since read.table() always returns a data.frame class, in the newer version of circlize, it is fine if the matrix represents as a data frame. When it is a data frame, the chordDiagram() will first check whether the number of columns is larger than 3 and all columns are numeric. If so, it will be converted to a matrix internally.
I am trying to write a simple application to encrypt/decrypt a buffer of unsigned char using OpenSSL RSA encryption. I have my public key and encrypt an array with
unsigned char plain [13] = "Hello World!";
unsigned char encrypted[1024]={};
unsigned char decrypted[1024]={};
int padding = RSA_PKCS1_OAEP_PADDING;
int flen = 13;
int res = RSA_public_encrypt(flen, plain, encrypted, rsa_pbk, padding);
where rsa_pbk is an RSA structure that contains the key. If I print it, the result is the following hexadecimal array:
13 d0 44 a3 2b 12 67 d8 e2 aa cf 53 6c 81 ed e9 9e 2d 9c dd 1d 28 84 5b 60 93 58 1c 7f eb b 66 26 39 8c 27 48 11 31 6 53 90 16 2e da 5c 7e 48 3e 15 c2 19 d3 10 79 71 1a fa f7 c1 57 93 82 f2 95 1 e d8 70 ba 1b 7e 12 d5 a 34 75 8f 2f 3c a6 60 f1 4b 60 6c 94 3e 4b 72 61 81 fb 89 e2 1e 5a 8 48 55 a5 5f 44 3b a4 e2 16 eb 7e 87 10 18 2e 1b 82 e7 86 43 69 21 ec a5 98 4 de 90 c5 5a 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
I am a little bit suspicious of the zeros at the end, but it could be ok. But, when I try to decrypt it with
flen = keysize - 50;
RSA_private_decrypt(flen, encrypted, decrypted, this->rsa_pvk, padding);
I get the following error
error:0407A079:rsa routines:RSA_padding_check_PKCS1_OAEP:oaep decoding error
error:04065072:rsa routines:RSA_EAY_PRIVATE_DECRYPT:padding check failed
that I'm not able to understand. Does anybody know what it means and why it arises?
Manual page states that: RSA_public_encrypt() returns the size of the encrypted data. RSA_private_decrypt() returns the size of the recovered plaintext. On error, -1 is returned; the error codes can be obtained by ERR_get_error(3).
Your code should look like this:
unsigned char plain [13] = "Hello World!";
unsigned char encrypted[1024]={};
unsigned char decrypted[1024]={};
int padding = RSA_PKCS1_OAEP_PADDING;
int flen = 13;
int res = RSA_public_encrypt(flen, plain, encrypted, rsa_pbk, padding);
flen = res;
res = RSA_private_decrypt(flen, encrypted, decrypted, this->rsa_pvk, padding);
Performing an snmpwalk on OID .1.3.6.1.4.1.2021.11 produces the following CpuRaw values:
UCD-SNMP-MIB::ssCpuRawUser.0 = Counter32: 3191634181
UCD-SNMP-MIB::ssCpuRawNice.0 = Counter32: 2586628
UCD-SNMP-MIB::ssCpuRawSystem.0 = Counter32: 480833488
UCD-SNMP-MIB::ssCpuRawIdle.0 = Counter32: 3578238833
UCD-SNMP-MIB::ssCpuRawWait.0 = Counter32: 461331879
UCD-SNMP-MIB::ssCpuRawKernel.0 = Counter32: 422462005
UCD-SNMP-MIB::ssCpuRawInterrupt.0 = Counter32: 7890770
UCD-SNMP-MIB::ssCpuRawSoftIRQ.0 = Counter32: 50480713
I note that all values, with the exception of idle + kernel, have matching values (close enough) in /proc/stat:
cpu 3191634876 2586629 422462086 7873206561 461331924 7890771 50480723 0
cpu0 1551975573 184783 190766514 1008267162 200070032 7243827 44073977 0
cpu1 610948559 324197 73381486 2228315579 87505437 51905 2054732 0
cpu2 494534866 1024716 75891701 2342123809 86260984 289810 2089023 0
cpu3 534175876 1052931 82422383 2294500009 87495469 305228 2262989 0
intr 8208380331 4267093007 3 0 4 4 0 0 0 3 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3538 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 328178999 0 0 0 0 0 0 0 997463093 0 0 0 0 0 0 0 2374098089 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 241543587 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
ctxt 163836165157
btime 1351100022
processes 224876777
procs_running 5
procs_blocked 0
I'd like to know what the difference is between ssCpuRawSystemand ssCpuRawKernel and how this relates to the contents of /proc/stat
ssCpuRawSystem
The number of 'ticks' (typically 1/100s) spent
processing system-level code.
On a multi-processor system, the 'ssCpuRaw*'
counters are cumulative over all CPUs, so their
sum will typically be N*100 (for N processors).
This object may sometimes be implemented as the
combination of the 'ssCpuRawWait(54)' and
'ssCpuRawKernel(55)' counters, so care must be
taken when summing the overall raw counters.
ssCpuRawKernel
The number of 'ticks' (typically 1/100s) spent
processing kernel-level code.
This object will not be implemented on hosts where
the underlying operating system does not measure
this particular CPU metric. This time may also be
included within the 'ssCpuRawSystem(52)' counter.
On a multi-processor system, the 'ssCpuRaw*'
counters are cumulative over all CPUs, so their
sum will typically be N*100 (for N processors).