Tips for writing ASP.NET performance code

I have been reading ASP.NET Performance by J.D. Meier et al (well it is the summer holidays :-)). I like these, they contain all the key performance engineering principles..
1) Avoid network round trips
2) Avoid unnecessary work
3) Hold shared resources for as short as possible
4) Do work where it needs to be done
5) Avoid creating hotspots and areas of contention.

Design coarse-grained services. Coarse-grained services minimize the number of client-service interactions and help you design cohesive units of work. Coarse-grained services also help abstract service internals from the client and provide a looser coupling between the client and service. Loose coupling increases your ability to encapsulate change. If you already have fine-grained services, consider wrapping them with a facade layer to help achieve the benefits of a coarse-grained service.
Minimize round trips by batching work. Minimize round trips to reduce call latency. For example, batch calls together and design coarse-grained services that allow you to perform a single logical operation by using a single round trip. Apply this principle to reduce communication across boundaries such as threads, processes, processors, or servers. This principle is particularly important when making remote server calls across a network.
Acquire late and release early. Minimize the duration that you hold shared and limited resources such as network and database connections. Releasing and re-acquiring such resources from the operating system can be expensive, so consider a recycling plan to support “acquire late and release early.” This enables you to optimize the use of shared resources across requests.
Evaluate affinity with processing resources.When certain resources are only available from certain servers or processors, there is an affinity between the resource and the server or processor. While affinity can improve performance, it can also impact scalability. Carefully evaluate your scalability needs. Will you need to add more processors or servers? If application requests are bound by affinity to a particular processor or server, you could inhibit your application’s ability to scale. As load on your application increases, the ability to distribute processing across processors or servers influences the potential capacity of your application.
Put the processing closer to the resources it needs. If your processing involves a lot of client-service interaction, you may need to push the processing closer to the client. If the processing interacts intensively with the data store, you may want to push the processing closer to the data.
Pool shared resources. Pool shared resources that are scarce or expensive to create such as database or network connections. Use pooling to help eliminate performance overhead associated with establishing access to resources and to improve scalability by sharing a limited number of resources among a much larger number of clients.
Avoid unnecessary work. Use techniques such as caching, avoiding round trips, and validating input early to reduce unnecessary processing.
Reduce contention. Blocking and hotspots are common sources of contention. Blocking is caused by long-running tasks such as expensive I/O operations. Hotspots result from concentrated access to certain data that everyone needs. Avoid blocking while accessing resources because resource contention leads to requests being queued. Contention can be subtle. Consider a database scenario. On the one hand, large tables must be indexed very carefully to avoid blocking due to intensive I/O. However, many clients will be able to access different parts of the table with no difficulty. On the other hand, small tables are unlikely to have I/O problems but might be used so frequently by so many clients that they are hotly contested. Techniques for reducing contention include the efficient use of shared threads and minimizing the amount of time your code retains locks.

Use progressive processing. Use efficient practices for handling data changes. For example, perform incremental updates. When a portion of data changes, process the changed portion and not all of the data. Also consider rendering output progressively. Do not block on the entire result set when you can give the user an initial portion and some interactivity earlier.
Process independent tasks concurrently. When you need to process multiple independent tasks, you can asynchronously execute those tasks to perform them concurrently. Asynchronous processing offers the most benefits to I/O bound tasks but has limited benefits when the tasks are CPU-bound and restricted to a single processor. If you plan to deploy on single-CPU servers, additional threads guarantee context switching, and because there is no real multithreading, there are likely to be only limited gains. Single CPU-bound multithreaded tasks perform relatively slowly due to the overhead of thread switching

Of course the devil is in the detail. You need to work out when it is worth caching something compared to recalculating the result. Where best to do processing is dependent on the user scenarios and the technology. Avoiding network round trips often requires a detail understanding of the architecture and complonets


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