Revision as of 20:16, 2 February 2018

Summary

LU Decomposition is an example of how to use Nu+ features to optimize an algorithm, using multithreaded and vectorial code.

Reusable code decomposition and multithreaded and vectorial optimization methods are shown; they can be applied to every other parallelizable algorithm, from image processing to machine learning ones.

Algorithm Description

LU Decomposition is used in solving systems of linear equations

Algorithm decomposes A in:

• upper triangolar matrix
• lower triangolar matrix

Partial Pivoting

Partial pivoting is an optimization of standard LU Decomposition that aims to reduce numerical instability

• Therefore a matrix is needed to keep track of pivoting operations
• Partial because pivoting is applied on rows only

These matrices have to verify the relation:

Pseudocode

nu+ Optimization

In this section, it is shown how the pseudo-code algorithm can be optimized using nu+ features.

Pivoting phase

The pivoting phase has been made multi-threaded. Every k-th iteration (k ranging from 0 to m-1) of the pivoting operations works on the elements belonging to the k-th column and to rows from k to m-1.

Less rows than threads

In the case rows are less than threads, the algorithm disables the exceeding threads and enables only as many threads as rows.

if (rowsTreated < THREADS) {
		if(id<rowsTreated-1)
		{
			v[id] = u[k+id][k];
			__builtin_nuplus_flush((int)&v[id]);
		}

A following barrier make disabled threads wait for working threads.

Rows are multiple of threads

Every thread is enabled, working on a number of rows defined by work_per_thread variable

	work_per_thread = rowsTreated / THREADS;
		if (rowsTreated % THREADS == 0) {
			for(int i=0; i<work_per_thread; i++)
			{
				v[k+id*work_per_thread+i] = u[k+id*work_per_thread+i][k];
				__builtin_nuplus_flush((int)&v[k+id*work_per_thread+i]);
				
			}

Rows are not multiple of threads

Every thread is enabled, making at least "work_per_thread" operation. Last thread (identified by the greatest thread_id) makes "extra_work" more operations.

			extra_work = work_per_thread % THREADS;
			for(int i=0; i<work_per_thread + (id==THREADS-1) * extra_work; i++)
			{		
				v[k+id*work_per_thread+i] = u[k+id*work_per_thread+i][k];
				__builtin_nuplus_flush((int)&v[k+id*work_per_thread+i]);
			}

Threads with id less than the maximum will complete their operations before the maximum id one, and will wait for its completion in next barrier.