Synchronisation in .NET– Part 3: Spin Locks and Interlocks/Atomics

In the previous instalments (Part 1 and Part 2) of this series, we have drawn some conclusions about both .NET itself and CPU architectures. Here is what we know so far:

• When there is contention on a single cache line, the lock() method scales very poorly and you get negative scale the moment you leave a single CPU core.
• The scale takes a further dip once you leave a single CPU socket
• Even when we remove the lock() and do thread unsafe operations, scalability is still poor
• Going from a class to a padded struct gives a scale boost, though not enough to get linear scale
• The maximum theoretical scale we can get with the current technique is around 90K operations/ms.

In this blog entry, I will explore other synchronisation primitives to make the implementation safe again, namely the spinlock and Interlocks. As a reminder, we are still running the test on a 4 socket machine with 8 cores on each socket with hyper threading enabled (for a total of 16 logical cores on each socket).

Synchronisation in .NET– Part 2: Unsafe Data Structures and Padding

In the previous blog post we saw how the lock() statement in .NET scales very poorly when there is a contention on a data structure. It was clear that a performance logging framework that relies on an array with a lock on each member to store data will not scale.

Today, we will try to quantify just how much performance we should expect to get from the data structure if we somehow solve locking. We will also see how the underlying hardware primitives bubble up through the .NET framework and break the pretty object oriented abstraction you might be used to.

Because we have already proven that ConcurrentDictionary adds to much overhead, we will focus on arrays as the backing store for the data structure in all future implementations.

Big/Big Table Joins

With the popularity of my last blog entry on Dangerous Joins, I felt inspired to write a bit more about the join strategies. Thanks for participating and reading, there seems to be a large appetite for Data Modeling out there – this blog now has over 7K unique visits every months.

Today, let us look at strategies for joining two big tables together.

Running Many Batch Statements in Parallel

When designing highly scalable architectures for modern machines, you will often need to do some form of manual parallelism control. Managing this is not always easy, but in this blog I will give you one piece of my toolbox to help you.

Let us walk through an example together, a tiny case study. This is a problem which many of you will be familiar with.