Skip to main content

The Cost of Type.GetType()

Most framework-style software spends an appreciable amount of time dynamically loading code. Some of this code is executed quite frequently. I've recently been working on a web framework where URLs map to type names and methods, so I've been digging into these sort of patterns a great deal lately.

The canonical means to map a type name to a System.Type instance is via System.Type.GetType(string). In a framework which performs a significant number of these lookups, it's not clear what sort of performance characteristics one can expect from this static framework function.

Here's the source for a simple test pitting Type.GetType() against a cache backed by a Dictionary<string, Type>. All tests were run on a Core 2 Duo 2.2 GHz, .NET CLR 3.5, and all numbers indicate the elapsed CPU ticks.

Type.GetType()Dictionary<string, Type>

Each program was run 20 times, and the resulting timing statistics were run through Peirce's Criterion to filter out statistical outliers.

You can plainly see that using a static dictionary cache is over two orders of magnitude faster than going through GetType(). This is a huge savings when the number of lookups being performed is very high.

Edit: Type.GetType is thread-safe, so I updated the test to verify that these performance numbers hold even when locking the dictionary. The dictionary is still two orders of magnitude faster. There would have to be significant lock contention in a concurrent program to justify using Type.GetType instead of a dictionary cache.


Popular posts from this blog

async.h - asynchronous, stackless subroutines in C

The async/await idiom is becoming increasingly popular. The first widely used language to include it was C#, and it has now spread into JavaScript and Rust. Now C/C++ programmers don't have to feel left out, because async.h is a header-only library that brings async/await to C!Features:It's 100% portable C.It requires very little state (2 bytes).It's not dependent on an OS.It's a bit simpler to understand than protothreads because the async state is caller-saved rather than callee-saved.#include "async.h" struct async pt; struct timer timer; async example(struct async *pt) { async_begin(pt); while(1) { if(initiate_io()) { timer_start(&timer); await(io_completed() || timer_expired(&timer)); read_data(); } } async_end; } This library is basically a modified version of the idioms found in the Protothreads library by Adam Dunkels, so it's not truly ground breaking. I've mad…

Easy Reverse Mode Automatic Differentiation in C#

Continuing from my last post on implementing forward-mode automatic differentiation (AD) using C# operator overloading, this is just a quick follow-up showing how easy reverse mode is to achieve, and why it's important.Why Reverse Mode Automatic Differentiation?As explained in the last post, the vector representation of forward-mode AD can compute the derivatives of all parameter simultaneously, but it does so with considerable space cost: each operation creates a vector computing the derivative of each parameter. So N parameters with M operations would allocation O(N*M) space. It turns out, this is unnecessary!Reverse mode AD allocates only O(N+M) space to compute the derivatives of N parameters across M operations. In general, forward mode AD is best suited to differentiating functions of type:RRNThat is, functions of 1 parameter that compute multiple outputs. Reverse mode AD is suited to the dual scenario:RN → RThat is, functions of many parameters that return a single real …

Easy Automatic Differentiation in C#

I've recently been researching optimization and automatic differentiation (AD), and decided to take a crack at distilling its essence in C#.Note that automatic differentiation (AD) is different than numerical differentiation. Math.NET already provides excellent support for numerical differentiation.C# doesn't seem to have many options for automatic differentiation, consisting mainly of an F# library with an interop layer, or paid libraries. Neither of these are suitable for learning how AD works.So here's a simple C# implementation of AD that relies on only two things: C#'s operator overloading, and arrays to represent the derivatives, which I think makes it pretty easy to understand. It's not particularly efficient, but it's simple! See the "Optimizations" section at the end if you want a very efficient specialization of this technique.What is Automatic Differentiation?Simply put, automatic differentiation is a technique for calculating the derivativ…