.NET news » Performance

How to use the DynamicMethod and ILGenerator classes to create dynamic code at runtime that outperforms reflection

Your own extensible and configurable Thread Pool

The try/catch block is used to capture thrown exceptions and to allow an application to recover gracefully from the problems that caused them. This article has been created to determine if the try/catch block causes a substantial performance penalty.

This article has two aims. Firstly, there are a series of five exercises that detail the process of successfully multi-threading a sequential algorithm with timely progress display in the UI. It also provides an implementation of a thread local storage class which can help realise the performance promise of many-core machines.

In this article, we will look into JIT optimizations, with specific focus on inlining.

A framework for object serializiation/deserialization that is many times faster and yields compact output.

Nowadays, multi-core processing is a growing industry trend as single core processors rapidly reach the physical limits of possible complexity and speed. It's obvious that if such wonderful hardware exists, it should be supported in .NET, shouldn't it? To check this I looked for a well-known easy-parallelizable algorithm. The benchmark should have handled different .NET parallelizing techniques with memory and mathematical operations. In my opinion, the best algorithms that fit these requirements are QuickSort and Matrix multiplication.

Please take into account, that this article doesn't describe any algorithms' optimization techniques. Instead it shows, how to parallelize your C# code and compares the performance of different approaches.

How much faster will implementing "the generic bad algorithm" in SSE2 make it?

There are a lot of ways to improve performance in web applications. One of the simplest but most effective methods is to cache images on the client. In this article I would like to show how we implemented image caching for our DotNetNuke website.

I work with performance-critical software that often requires some form of cache to function efficiently. However, there is also often a need to constrain memory usage, so I set out to create a high-performance cache with a maximum size.