My Conclusions on Parallel Computing

We have been dealing with parallel computing for some while now. Some of the ideas we had at the start proved to be wrong while others are only becoming relevant in the near future. No doubt about it, parallel computing was pushed and forced into the mainstream of computing just as Object Oriented was in the previous millennia.

Some History: Hardware

The first to deal with parallel computing were hardware developers because the hardware supports multiple devices working at the same time, with different operation rates and response times. Hardware design is also Event Driven because devices work independently and issue an Interrupt event when required. The computer hardware we know today is fully parallel however it is centralized with a single CPU (Central Processing Unit) and multiple peripheral devices.

Some History: Kernel

The next to support parallel computing was the software infrastructure which in modern operating systems is the Kernel. The Kernel must support multiple events coming in the form of Hardware Interrupts and propagated upwards as Software Events. Kernels are commonly distributed in design as several Drivers can communicate with each other. The centralized object in the system is allowing communication between the drivers and supports synchronization but is not supposed to contribute to the application's business logic in any form or way.

Some History: Network

UNIX is based on services. A Service is a way to call a function over network. Network technologies required distributed design in which every element is completely parallel to the next and there is no single 'processor unit' as the system's master. UNIX took this to the next level with technologies such as services, pipes, sockets, mailslots, Fork and more. At a time when programming was a tedious work, developing an operating system to support Fork meant extensive efforts. Still UNIX had built in support for that mechanism which solves so many problems... Only we forgot how to use it and I don't remember seeing a new system design that had Fork in it.

Some History: Applications

When I just started with C programming and have just found out about threads I tried doing things in parallel just to see how it works. The result was, as you can imagine, by far worse. The application runs much slower, there are "Random Bugs" and the code looks terrible. The explanation I got was that there is only one CPU and the different threads compete over it. No Multi-Core CPU means that there is no ROI (return on investment) for using multiple threads and the large efforts required for a parallel design. The only reason to use a thread is when you really have to for example when there is need to wait for hardware or a network buffer.

Parallel Computing Today

A few years ago CPUs got to a certain hardware limitation which would have required special cooling. At this point the race to reduce silicon size and increase clock frequency has ended. Instead of spending massive amounts of silicon on the CPU for advanced algorithms to improve instruction pre-fetch, smaller and simpler CPUs are used and there is room for more CPUs on the same silicon wafer. We got the Multi-Core CPU which practically means several CPUs on the same computer.

At first the cores of a Multi-Core CPU were simpler than the single core one. These cores also operated in a much lower frequency which meant that an application designed for a single task operation had a massive performance impact when moving to a new computer, for the first time ever.

Parallel Computing has become main stream. We started with a long series of lectures about parallel computing. It seemed that people wanted to know about this subject but there was so much overhead that Parallel Computing simply scared people away. There is a huge ramp before you can be a good parallel programmer. Just as there is for object oriented programming. This meant that team leaders and architects were at the same level as beginner programmers, or perhaps with some very little advantage. Add to this the fact that there are massive amounts of code already written for a single core CPU and good advantages can be achieved after at least some re-write. Last but most important reason to reject parallel computing was that it is easier and cheaper to buy another machine than to make the best out of the CPU cores. This was actually a boost for Cloud Computing.

Who is doing Parallel Computing

There are several types of parallel computing. The hardware is parallel so the Kernel is parallel. With this type of parallelism every worker is doing something else, and workers own their resources instead of sharing them. For a long while now DSP (Digital Signal Processing) chips are Multi-Core CPUs so that the algorithms executed on these chips can run faster. Algorithms and DSP chips are evaluated by MIPS which is the amount of instructions per time constant. Gaining performance increase with an algorithm means either using less instructions or adding more worker CPU cores. PCs also run algorithms such as face recognition, image detection, image filtering, motion detection, and more. The transition from single core CPU to a Multi-Core CPU was fast and simple.

Algorithm's increase in performance is relative to the amount of computations per data item. More computation more cores can be used. Image Blending (fade) is an example for an algorithm which cannot enjoy the use of more than a single core. Take an image and blend each pixel with the corresponding pixel of another image. Each pixel should be read from RAM then a simple addition and shift right are performed and then the result should be writen back to RAM. The CPU can operate at a rate of 3GHz and the RAM at 1GHz. For each pixel in the image we: Read pixel A, Read pixel B, Add, Shift, Write result pixel. Add another core and the CPU cores will mutually block on access to the memory. This is also true for Databases and database algorithms such as sort algorithms, linked lists, etc. For this reason the new Multi-Core CPUs have extensive support for parallel access to memory.

Parallel Computing ROI

Parallel Computing is the new future for computers. Object Oriented is no longer the new buzz word. I keep telling people that before they make an Object Oriented Design to their systems they should make flow charts. Good OOD is based on good system flow charts, whether you write them down or do it in your head as an art.

We all used to think that User Interface is the product and OOD is the way to do it. It now looks like we were wrong:

User Experience is the prodcut and Parallel Design is the way to do it. User Experience (UX) is not User Interface (UI). User Interface defines what the product would look like, or in other words UI defines what the product is. Object Oriented Design defines what the code looks like, or in other words OOD defines what the code is. Parallel Computing defines how the code works, or in other words Parallel Computing defines what the code does. User Experience defines how the application behaves, or in other words User Experience defines what the application does.

I am not using a C++ library because it is using linked-lists. I am using that library because it can sort.

I am not buying a product because it looks like I want it to look, for this I can buy a framed picture instead. I am buying a product because it is doing something I need and it is not doing what I do not need.

Parallel Computing is the basis for User Experience. Even if you have a single core it is better to have good parallel design. As customers you know this, you don't want to accidentally hit "Print" instead of "Save" and now wait for 5 seconds punishment for the dialog to open so you can close it. (see minute 43 for demo video)

Today we have so many good resources and tools. Now is the time to learn how to work parallel and produce good prodcuts with good UX.

For more complete information about compiler optimizations, see our Optimization Notice.