Revision as of 12:15, 5 January 2018

Group Members

Intel Parallel Studio vTune Amplifier

What is VTune Amplifier?

A tool created by Intel to provide performance analysis on software.
Offers both a GUI and command-line version for both Windows and Linux
GUI only for OSX
Basic features available on both Intel and AMD processors, but advanced features only for Intel

How to use it?

Available as a standalone unit or part of the following packages:
- Intel Parallel Studio XE Cluster Edition and Professional Edition
- Intel Media Server Studio Professional Edition
- Intel System Studio

Can be run on a local machine

Hotspots

Basic hotspot analysis

We used our workshop 6 as an example to demonstrate this particular aspect of Intel Vtune Amplifer

Advanced hotspot analysis

Parallelism

Concurrency

Best for visualizing thread parallelism on available cores, finding areas with high or low concurrency, and identifying serial bottlenecks in your code
Provides information on how many threads were running at each moment during application execution
Includes threads which are currently running or ready to run and therefore are not waiting at a defined waiting or blocking API
Also shows CPU time while the hotspot was executing and estimates its effectiveness either by CPU usage or by Threads Concurrency

Results of Concurrency tests on Workshop 6

I ran the Concurrency test on each of the functions in Workshop 6. I isolated each function by commenting out all others, then ran them 1 by 1. This was to get an idea of how they perform on their own. Finally I ran them all together to see how the program runs overall.

matmul_0 (Serial)

double matmul_0(const double* a, const double* b, double* c, int n) {
	for (int i = 0; i < n; i++) {
		for (int j = 0; j < n; j++) {
			double sum = 0.0;
			for (int k = 0; k < n; k++)
				sum += a[i * n + k] * b[k * n + j];
			c[i * n + j] = sum;
		}
	}
	double diag = 0.0;
	for (int i = 0; i < n; i++)
		diag += c[i * n + i];
	return diag;
}

matmul_1 (Serial with j-k loops reversed)

double matmul_1(const double* a, const double* b, double* c, int n) {
	
	for (int i = 0; i < n; i++) {
		for (int k = 0; k < n; k++) {
			double sum = 0.0;
			for (int j = 0; j < n; j++)
				sum += a[i * n + k] * b[k * n + j];
			c[i * n + k] = sum;
		}
	}
	double diag = 0.0;
	for (int i = 0; i < n; i++)
		diag += c[i * n + i];
	return diag;
}

matmul_2 (Cilk Plus with cilk_for)

double matmul_2(const double* a, const double* b, double* c, int n) {
	
	cilk_for (int i = 0; i < n; i++) {
		cilk_for (int j = 0; j < n; j++) {
			double sum = 0.0;
			for(int k = 0; k < n; k++) {
				sum += a[i * n + k] * b[k * n + j];
			}
			c[i * n + j] = sum;
		}
	}

	double diag = 0.0;
	for (int i = 0; i < n; i++)
		diag += c[i * n + i];
	return diag;
}

matmul_3 (+array notation, reducer)

double matmul_3(const double* a, const double* b, double* c, int n) {
	
	cilk_for(int i = 0; i < n; i++) {
		cilk_for(int j = 0; j < n; j++) {
			double sum = 0.0;
			for (int k = 0; k < n; k++) {
				sum += a[i * n + k] * b[k * n + j];
			}
			c[i * n + j] = sum;
		}
	}

	cilk::reducer_opadd <double> diag(0.0);
	cilk_for(int i = 0; i < n; i++) {
		diag += c[i * n + i];
	}
	return diag.get_value();
}

matmul_4 (+vectorization)

double matmul_4(const double* a, const double* b, double* c, int n) {
	
	cilk_for(int i = 0; i < n; i++) {
		cilk_for(int j = 0; j < n; j++) {
			double sum = 0.0;
#pragma simd
			for (int k = 0; k < n; k++) {
				sum += a[i * n + k] * b[k * n + j];
			}
			c[i * n + j] = sum;
		}
	}

	cilk::reducer_opadd <double> diag(0.0);
	cilk_for(int i = 0; i < n; i++) {
		diag += c[i * n + i];
	}
	return diag.get_value();
}