1548055860

# Why is a single Thread faster than multithreading in my code?

My code was very slow, so I thought i could push it with multithreading. But it got slower when I used multithreading. It is for a convolution operation. The Matrix[] in length is about 1 to 64 and each Matrix is 28x28 big. Also the Matrix[][] kernel length is 1 to 64 in the first dimension and also in the second dimension and each kernel is 3x3 big.(Matrix.matrix is a double[][]-array)

I already tried using Executorservice, but there was the same problem.

```public static Matrix[] convolve(Matrix[] in, Matrix[][] kernel) {
```// Defining size of output matrix
int kr = kernel[0][0].rows - 1;
int kc = kernel[0][0].cols - 1;

Matrix[] out = new Matrix[kernel.length];

for (int i = 0; i &lt; kernel.length; i++) {
out[i] = new Matrix(in[0].rows - kr, in[0].cols - kc);
}

// Convolution operation
for (int i = 0; i &lt; out[0].rows; i++) {
for (int j = 0; j &lt; out[0].cols; j++) {

double sum = 0;
for (int m = 0; m &lt; kernel.length; m++) { // Size of filters
for (int n = 0; n &lt; kernel[m].length; n++) { // Depth of filters
for (int k = 0; k &lt; kernel[m][n].rows; k++) { // Stride over
for (int l = 0; l &lt; kernel[m][n].cols; l++) { // Stride over
sum += in[n].matrix[i + k][j + l] * kernel[m][n].matrix[k][l];
}
}
}
out[m].matrix[i][j] = sum;
}

}
}

return out;
```
}
public Matrix[] convolveWithThreads(Matrix[] in, Matrix[][] kernel) {
// Defining size of output matrix
int kr = kernel[0][0].rows - 1;
int kc = kernel[0][0].cols - 1;
```Matrix[] out = new Matrix[kernel.length];

for (int i = 0; i &lt; kernel.length; i++) {
out[i] = new Matrix(in[0].rows - kr, in[0].cols - kc);
}
// Convolution Operation
for (int t = 0; t &lt; kernel.length; t++) {
final int m = t;

@Override
public void run() {
for (int i = 0; i &lt; out[0].rows; i++) {
for (int j = 0; j &lt; out[0].cols; j++) {

double sum = 0;
for (int n = 0; n &lt; kernel[m].length; n++) { // Depth of filters
for (int k = 0; k &lt; kernel[m][n].rows; k++) { // Stride over
for (int l = 0; l &lt; kernel[m][n].cols; l++) { // Stride over
sum += in[n].matrix[i + k][j + l] * kernel[m][n].matrix[k][l];
}
}
}
out[m].matrix[i][j] = sum;
}
}
}
});
th.start();
}

for (Thread t : ar) {
try {
t.join();
} catch (InterruptedException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}

return out;
```
}
```

}

Without threads it can do 70000 operations in five minutes and with threads it can only do 40000 operations. (Matrix[] in length = 8 and Matrix[][] kernel length = 8 and 8)

## Buddha Community

1548140969

Spawning a thread and running the thread has its own overhead and consume the resources. So it will slow down your program that otherwise gets executed in a single thread.

For eg: If your program was slow because of a blocking operation,then using a thread would have cut down overall execution time.

Note: Use executorservice- with fixed thread pools and schedule the worker tasks, and dont create the threads inside the loop thread creation has its own overhead, it will be slow.

1604008800

## Static Code Analysis: What It Is? How to Use It?

Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.

Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.

“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”

• J. Robert Oppenheimer

### Outline

We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.

We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.

Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use `ast` module, and wide adoption of the language itself.

### How does it all work?

Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:

As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:

### Scanning

The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.

A token might consist of either a single character, like `(`, or literals (like integers, strings, e.g., `7``Bob`, etc.), or reserved keywords of that language (e.g, `def` in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.

Python provides the `tokenize` module in its standard library to let you play around with tokens:

Python

1

``````import io
``````

2

``````import tokenize
``````

3

4

``````code = b"color = input('Enter your favourite color: ')"
``````

5

6

``````for token in tokenize.tokenize(io.BytesIO(code).readline):
``````

7

``````    print(token)
``````

Python

1

``````TokenInfo(type=62 (ENCODING),  string='utf-8')
``````

2

``````TokenInfo(type=1  (NAME),      string='color')
``````

3

``````TokenInfo(type=54 (OP),        string='=')
``````

4

``````TokenInfo(type=1  (NAME),      string='input')
``````

5

``````TokenInfo(type=54 (OP),        string='(')
``````

6

``````TokenInfo(type=3  (STRING),    string="'Enter your favourite color: '")
``````

7

``````TokenInfo(type=54 (OP),        string=')')
``````

8

``````TokenInfo(type=4  (NEWLINE),   string='')
``````

9

``````TokenInfo(type=0  (ENDMARKER), string='')
``````

(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)

#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer

1621137960

## Guidelines for Java Code Reviews

#### Get a jump-start on your next code review session with this list.

Having another pair of eyes scan your code is always useful and helps you spot mistakes before you break production. You need not be an expert to review someone’s code. Some experience with the programming language and a review checklist should help you get started. We’ve put together a list of things you should keep in mind when you’re reviewing Java code. Read on!

### 5. Handle Exceptions With Care

#java #code quality #java tutorial #code analysis #code reviews #code review tips #code analysis tools #java tutorial for beginners #java code review

1604088000

## How to Find the Stinky Parts of Your Code (Part II)

There are more code smells. Let’s keep changing the aromas. We see several symptoms and situations that make us doubt the quality of our development. Let’s look at some possible solutions.

Most of these smells are just hints of something that might be wrong. They are not rigid rules.

This is part II. Part I can be found here.

### Code Smell 06 - Too Clever Programmer

The code is difficult to read, there are tricky with names without semantics. Sometimes using language’s accidental complexity.

_Image Source: NeONBRAND on _Unsplash

Problems

• Maintainability
• Code Quality
• Premature Optimization

Solutions

1. Refactor the code
2. Use better names

Examples

• Optimized loops

Exceptions

• Optimized code for low-level operations.

Sample Code

Wrong

``````function primeFactors(n){
var f = [],  i = 0, d = 2;

for (i = 0; n >= 2; ) {
if(n % d == 0){
f[i++]=(d);
n /= d;
}
else{
d++;
}
}
return f;
}
``````

Right

``````function primeFactors(numberToFactor){
var factors = [],
divisor = 2,
remainder = numberToFactor;

while(remainder>=2){
if(remainder % divisor === 0){
factors.push(divisor);
remainder = remainder/ divisor;
}
else{
divisor++;
}
}
return factors;
}
``````

Detection

Automatic detection is possible in some languages. Watch some warnings related to complexity, bad names, post increment variables, etc.

#pixel-face #code-smells #clean-code #stinky-code-parts #refactor-legacy-code #refactoring #stinky-code #common-code-smells

1625038259

## Thread Gauge Calibration - What Should You Know?

If you are using thread gauges or thread ring gauges you would have already heard about calibration. If you do not pay attention to thread gauge calibration then the integrity of the thread gauges you are purchasing and using could be compromised. In case you do not know what is thread gauge calibration and what is its significance then here are a few important factors that you should know about calibration.

When you order a custom trapezoidal thread gauge or a custom Whitworth thread gauge or for that matter any thread gauge, how will you know that it is exactly matching your requirements and specifications? As you know the thread gauges are inspection tools and unless they are 100% accurate there is no use having a thread gauge. When you calibrate the thread gauge you will know whether or not the tool is true to its specifications and whether it matches the required specifications 100%.

You may need to go for recalibration every time you repair your thread gauge or make any modifications to it. In case you are experiencing sudden episode of issues with your threaded components then it is best to first check the thread gauges you are using to ensure that the problem is not with the thread gauge.

It is also important to get a calibration certificate for your thread gauge if the tool has been dropped or if someone that is not trained properly use the thread gauge and exerts undue force. In other words, whenever you suspect that the thread gauge could have been damaged then it is vital go check the thread gauge and calibrate it so that you can be sure of the accuracy of the thread gauge.

Always source all your thread gauges from the most trusted companies. Whenever you are calibrating your thread gauge get it done from a reputed calibration center. You cannot afford to have a faulty thread gauge as it is an inspection tool, a standard that is used to measure the accuracy of the other tools. Therefore, make sure that your thread gauges are always well maintained and regularly calibrated.

1604048400

## Softagram - Making Code Reviews Humane

The story of Softagram is a long one and has many twists. Everything started in a small company long time ago, from the area of static analysis tools development. After many phases, Softagram is focusing on helping developers to get visual feedback on the code change: how is the software design evolving in the pull request under review.

### Benefits of code change visualization and dependency checks

While it is trivial to write 20 KLOC apps without help of tooling, usually things start getting complicated when the system grows over 100 KLOC.

The risk of god class anti-pattern, and the risk of mixing up with the responsibilities are increasing exponentially while the software grows larger.

To help with that, software evolution can be tracked safely with explicit dependency change reports provided automatically to each pull request. Blocking bad PR becomes easy, and having visual reports also has a democratizing effect on code review.

Example visualization

### Basic building blocks of Softagram

• Architectural analysis of the code, identifying how delta is impacting to the code base. Language specific analyzers are able to extract the essential internal/external dependency structures from each of the mainstream programming languages.

• Checking for rule violations or anomalies in the delta, e.g. finding out cyclical dependencies. Graph theory comes to big help when finding out unwanted or weird dependencies.

• Building visualization for humans. Complex structures such as software is not easy to represent without help of graph visualization. Here comes the vital role of change graph visualization technology developed within the last few years.

#automated-code-review #code-review-automation #code-reviews #devsecops #software-development #code-review #coding #good-company