Schlagwort-Archive: high performance programming

Can Programs Be Made Faster?

Short answer: No. But, more efficient.

A happy new year to all of you! This is the first post in 2014 and it is a (not so) short post about a topic which follows me all the time during discussions about high performance computing. During discussions and in projects I get asked about how programs can be programmed to run faster. The problem is, that this mind set is misleading. It always takes me some minutes to explain the correct mind set: Programs cannot run faster, but more efficient to save time.

If we neglect that we can scale vertically by using faster CPUs, faster memory and faster disks, the speed of a computer is constant (by also neglecting CPUs which change there speed so save power). All programs run always with the same speed and we cannot do anything to speed them up by just changing the programming. What we can do is, to use the hardware we got as efficient as possible. The effect is: We get more done in less time. This reduces the program run time and the software seem to run faster. That is what people mean, but looking on efficiency brings the mind set to find the correct leverages on how to decrease run time.

A soon as a program returns the correct results it is effective, but there is also the efficiency which is to be looked at. Have a look to my post about effectiveness and efficiency for more details about the difference between effectiveness and efficiency. To gain efficiency, we can do the following:

Use all hardware available

All cores of a multi-core CPU can be utilized and all CPUs of the system if we have more than one CPU in the system. GPU or physical accelerator cards can be used for calculation if present.

Especially in brown field projects, where the original code comes from single core systems (before 2005 or so) or system which did not have appropriate GPUs (before 2009), developers did not pay attention multi-threaded, heterogeneous programming. These programs have a lot of potential for performance gains.

Look out for:

CPU utilization

Introduce mutli-thread programming into your software. Check the CPU utilization during an actual run and look for CPU idle tines. If there are any, check your software whether it can do something at the time the idle times occur.

GPU utilization

Introduce OpenCL or CUDA into your software to utilize the GPU board or physics accelerator cards if present. Check the utilization of the cards during calculation and look for optimizations.

Data partitioning for optimal hardware utilization

If a calculation does not need too much data, everything should be loaded into memory to have the data present there for efficient access. Data can also organized to have access in different modes for sake of efficiency. But, if there are calculations with amounts of data which do not fit into memory, a good strategy is needed for not to perform calculations on disk.

The data should be partitioned into smaller pieces. These pieces should fit into memory and the calculations on these pieces should run in memory completely. The bandwidth CPU to memory is about 100 to 1000 faster than CPU to disk. If you have done this, check with tools for cache misses and check whether you can optimize this.

Intelligent, parallel data loading

The bottle neck for calculations are CPU and/or GPU. They need to be utilized, because only they bring relevant results. All other hardware a facilities around that. So, do everything to keep the CPUs and/or GPUs busy. It is not a good idea to load all data into memory (and let CPU/GPU idle), then start a calcuation (everything is busy) to store the results afterwards (and have the CPU/GPU idle again). Develop you software with dynamic data loading. During the time calculations run, new data can be caught from disk to prepare the next calculations. The next calculations can run during the time the former results are written onto disk.This maybe keeps a CPU core busy with IO, but the other cores do meaningful work and the overall utilization increases.

Do not do unnecessary things

Have a look to my post about the seven muda to get an impression about wastes. All these wastes can be found in software and these lead into inefficiency. Everything which does not directly contribute to the expected results of the software needs to be questioned. Everything which uses CPU power, memory bandwidth and disk bandwidth, but is not directly connected to the requested calculation may be treated as potential waste.

To have a starter look for, check and optimize:

Decide early

Decide early, when to abort loops, what calculations to do and how to proceed. Some decisions are made in code on a certain position, but sometimes these checks can be done earlier in code or before loops, because the information is already present. This is something to be checked. During refactorings there might be other, more efficient positions for these checks. Look out for them.

Validate economically

Do not check in functions the validity of your parameters. Check the model parameters at the beginning of the calculations. Do it once and thoroughly. If these checks are sufficient, there should be no illegal state afterwards related to the input data. So they do not need to be checked permanently.

Let it crash

Check only input parameters of functions or methods if a fail of those be fatal (like returning wrong results). Let there be a NullPointerException, IllegalArgumentException and what so ever if something happens. This is OK and exceptions are meant for situations like that. The calculation can be aborted that way and the exception can be caught in a higher function to abort the software or the calculation gracefully, but the cost to check everything permanently is high. On the other side: What will you do when a negative value come into a square root function with double output or the matrix dimensions do not fit in a matrix multiplication? There is no meaningful way to proceed, but to abort the calculation. Check the input model and everything is fine.

Crash early

Include sanity checks in your calculations. As soon as the calculation is not bringing more precision, runs into a wrong result, gives the first nan or inf values or behaves strangely in any way, abort the calculation and let the computer compute something more meaningful. It is a total waste of resources to let a program run, which does not do anything meaningful anymore. It is also very social to let other people calculate stuff in the meantime.

Organize data for efficient access

I have seen software which looks up data in arrays element wise by scanning from the first element to the position where the data is found. This leads into linear time behavior O(n) for the search. This can be done with binary search for instance which brings logarithmic time behavior O(log(n)). Sometimes, it is also possible to hold data in memory in a not normalized way to have access to it in different ways. Sometimes a mapping is needed from index to data and sometimes the other way around. If memory is not an issue, think about keeping the data in memory twice for optimized access.

Conclusion

I hope, I could show how the focus on efficiency can bring the right insights on how to reduce software run times. The correct mind set helps to identify the weak points in software and the selection of the points above should point out some directions to look into software to find inefficiencies. A starting point is presented, but the way to go is different for every project.