CS3014 Lab 1: Sparse parallel multichannel multikernel convolution
26th February 2020
Modern deep neural networks (DNNs) are one of the most successful types of artificial intelligence. There are many artificial intelligence competitions each years in areas such as image processing, and pretty much all the winning entries to these competitions are DNNs. An important type of DNN that is very successful at image classification and other image processing tasks is the convolutional neural network (CNN).
A CNN consists of a directed acyclic graph of "layers" which are typically selected from a small number of standard layers such as activation layers, pooling layers, fully-connected layers, and convolutional layers. CNNs require very large amounts of computation to process and input image, and most of this time is spent in convolution layers.
The convolution layers are similar to, but nonetheless different from, standard two-dimensional convolution. In CNNs the images have multiple "channels" which might be red, green and blue, or simply notional different pieces of information about a single pixel of the image. In addition, CNN convolution is always performed with multiple different convolution kernels, each of which has multiple channels. We therefore refer to it as multichannel multikernel convolution.
The purpose of this labs is to better understand vectorization, code optimization and parallelization by parallelizing and optimizing an efficient convolution routine.
Our target machine is stoker.scss.tcd.ie. This machine has four processors. Each processor has eight out-of-order pipelined, superscalar cores. And each core has two-way simultaneous multithreading.
Some students have travelled home to another country and may have difficulty accessing stoker. In this case, please complete the project on whatever multicore machine you have access to. If this is a Linux machine, please include a copy of the /proc/cpuinfo file on that Linux machine. If it's not a Linux machine, please mention the processor model and number of cores in your project submission.
To compile with SSE on stoker you should:
- #include<x86intrin.h> in your C/C++ code
- compile your program with: gcc -O3 -msse4 file.c To comile with OpenMP, add the flag -fopenmp.
To write an efficient sparse multichannel multikernel convolution routine you will need an efficient basic algorithm, but it is also important that you take account of issues such as locality of data access and multiple available processor cores.
Your routine should be written in C/C++, and work on the same data structures as are used in the sample code. You may consult books, papers and online sources, but ALL SOURCES SHOULD BE ACKNOWLEDGED IN YOUR SUBMISSION. All the submitted code should be your own.
The work for this lab should be carried out in pairs, and each pair of students will make a joint submission. The submission should take the form of: (1) a working, well-written, commented piece of code, and (2) a document of a couple of pages describing the efforts made to make the code efficient. (3) a set of slides in PDF format that document your parallelization and optimization strategy, and list you execution times on the standard input sizes.
Please give the PDF file that contains your slides a filename based on the first names of those who are part of the team. Please use the first name that you are registered under in College rather than another first name (with apologies to those who use a different name or a different form of their name in practice).
Your routine should be capable of operating efficiently on a range of matrix sizes and shapes. The range of sizes we will consider are:
image_width: 16..512 image height: 16..512 kernel order: 1, 3, 5, or 7 number of channels: 32..2048 (always powers of 2) number of kernels: 32..2048 (always powers of 2) nz_ratio: 20..1000
You must use the following test harness to test and time your code: https://www.scss.tcd.ie/David.Gregg/cs3014/labs/conv-harness.c
The deadline for submitting your work is 23.59 Wednesday 8th April 2020.
To submit the lab, please send me an email with the code, the report, and your slides as attachments. Please make sure that your email has a subject line with the following format: HEDGEHOD
The list of names in your group should be the names that you are registered under in TCD, with each name separate by a comma. So, for example, a group of two might have have a subject line like the following: HEDGEHOG Cordelia Chase, Rupert Giles
A team of one might have a subject line like: HEDGEHOG Alexander Harris