Thursday, August 19, 2004

 

Multi threaded programming

Introduction

The major issue in multi threaded programming is synchronization of access to shared resources, otherwise called locking. I talk about locking issues in this document.

Locking In General

Before going to Windows specific locking issues, some general thoughts on locking is in order.

The most important rule in locking is: Lock your data, not code. Meaning, don't try to identify critical areas of code that requires serialization, identify the shared resources and lock access to those resources. Locking code seriously affects scalability.

It is important to realize that locking is a critical issue affecting scalability of your program. The worst case locking method is to require a single global lock for everything, which means effectively only one thread is operating at a time. This is clearly not what you want.

The best thing you can do to improve scalability is not to use locks at all. Well, it is almost impossible to achieve this, but you should work hard to get closer to this goal. The most effective method to achieve this is to use immutable objects. When you design a data structure, divide it into shared and non-shared units. Each thread can lock the shared unit, extract the non-shared unit (or a copy of it), and release the lock. Now this thread would continue doing its work without any lock.

An exercise in locking

Let us explore how one can implement locking in a typical network server application. In this server, we use a receive queue where requests received from the network are stored. We have multiple threads to extract and process requests in this queue and send a response packet to the clients. Our goal here is to minimize locking to the most important resource here, the queue itself. Let us look at different locking strategies.

The least efficient option would be to require each thread to lock the receive queue for the entire processing the request. Since the lock is held for the entire length of processing, no other thread can get in between, and this effectively becomes a single threaded solution.

In the case where the requests in the receive queue are independent of each other and need not be processed in the same order received: each thread can lock the queue, remove the request, unlock the queue and then process the request. Here the queue is locked only until the request is removed and no lock is held during processing. This solution scales better.

In the case where requests are to be processed in the order received : we can follow the earlier strategy but we need to make sure the send operation is serialized. To achieve this, we can create a send queue and associate a thread with it. We have to fill the send queue in the same order the packets are received (We can use partially filled send packets for this, the send thread would wait for the send packets to be filled in completely before sending it). You would probably need to use events to communicate with the send queue thread.

In the case where packets for different channels are received in the same queue: the requests within a single channel are to be processed in order. You can design it in such a way that only a single thread may operate on a channel at a time (no thread affinity here, a thread starting on one channel would complete all the requests on that channel). Instead of allocating a thread per channel, a thread working on a particular channel picks up all the pending requests of that channel. To achieve this, a thread pulls a request from the queue, looks at a per channel request queue for that channel (a separate queue), if this queue is empty then processes the request, if not add it to this per channel queue and go back to the main receive queue. On completing the processing of a request, each thread looks at the per channel queue of the request just completed, if a request is found there, processes the request, this repeats until the queue is empty. This is a slightly complicated method with its own problems. If you have more channels than threads, some channels may starve. If processing a request is time consuming, throughput would be slow with a possibility of idle threads. It is going to balancing act, depending on your situation.

Just as a note of caution, when you work with queues always consider the head of line blocking issue. A problem with the first element in the queue blocks other entries in the queue from being processed.

Locking order

Some developers recommend a locking order. To avoid deadlocking, they recommend that you grab lock A before you grab lock B. In my opinion, if you have to define a locking order, you made a mistake in your design. If you design your locks to be less granular and try to minimize the time you hold on to the locks, you should never run into this problem. Again remember the rule, lock your data not code.

Locking and Callbacks

In a typical server application, you need to call some external functions during your processing of data. Since you don't know what these functions might do, you should not be holding your lock during these callbacks. A common way to solve this problem is to release the lock before calling the external function and then grab the lock again. The problem with this approach is, when you grab the lock the again, the associated resource (and the lock itself) might have been deleted. You need to use reference counts and flags to solve this problem and the code gets complicated and buggier.

A better approach would be to complete all your processing, release the lock and then invoke the callback. You can achieve this by cloning data and using multiple locks.

If you use fine granular locks, you can even invoke the callback while still holding the lock. You can use this technique if you have access to the source code for the callback function and you can make sure it doesn't do any abnormal thing like sleeping.

Reference Counts

Reference counts are a widely used technique to solve resource sharing problems. It is so easy to use, developers don’t hesitate to use it. Reference counts are long term locks, they span multiple calls, they are mainly used to prevent a resource from accidentally getting deleted. You increment the reference count for the duration of an object's use, you decrement it when you no longer need the resource. Whenever the reference count becomes zero, it is deleted. So when an object is created, the reference count is 1, and when it is deleted by the user the reference count is just decremented, object gets deleted only when reference count is zero.

But reference counts are a debugging nightmare, while they are easy to code, they are very difficult to maintain. With bad coding, a resource can be accidentally deleted or never deleted. So try to avoid reference counts if possible. If you have to use reference counts, use separate flags for each user of the resource, when you increment a reference count set the associated flag, and when you decrease the reference count, clear the flag. This way when you are debugging, you can find out who is holding the resource.

If the number of users of a resource is small and they are part of the same application, you can use flags instead of reference counts. They may be difficult to code, but presents fewer opportunities for bugs.

Locking in Windows

Windows provides a rich set of locking instruments. You can use many kernel objects for locking, mutex being the most common. You could also use events and semaphores for locking. There is enough documentation on MSDN on how to use these kernel objects. If you need to learn more about object manager and kernel objects, read the Inside Windows book by Mark Russinovich and David Solomon.

If your application contains multiple processes, you need to use kernel objects. But if your application is multi threaded, you can (and should) use critical sections. I wish Microsoft named CriticalSection as UserLock or something, it gives the impression that you should lock code instead of data.

A critical section is basically a data structure to be defined along with other fields in your own data structure. A critical section is a smart object, it uses a combination of Interlocked operations and kernel objects (Interlocked operations are discussed later). Locking a critical section object may not make a kernel transition, so it is faster than a kernel object. Kernel object is used only if the object is already held by another thread. This is the reason why you should use a critical section in a multi threaded program instead of a kernel object. Since a critical section uses addresses from its own process, it can't used between processes.

Critical sections are recursive, if a thread already holds a critical section, trying to grab the critical section again would succeed.

To use a critical section, you should first initialize it by calling InitializeCriticalSection(). To lock the critical section, call EnterCriticalSection(), to unlock critical section call LeaveCriticalSection(). At the end of your program call DeleteCriticalSection() to release the resources.

Interlocked operations

Interlocked operations take advantage of the CPU LOCK keyword to implement 32-bit operations atomically (there are 64-bit variants too). The most common of these are InterlockedIncrement and InterlockedDecrement, they increment and decrement a 32-bit value at an address atomically, that means no processing occurs in the middle of interlocked operations. If it is not atomic, some thread might change the value between your read and write.

Interlocked operations come handy when you try to synchronize access to a 32 bit value. They are fast, but I still prefer to use critical sections instead primarily because they can't be used to protect their containing data structure. For simple things like incrementing a reference count, these may be useful. Critical sections make the code more readable and in a complicated project it is safer (with multiple programmers working on a single project, it's easier to introduce a bug with interlocked operations).

To get a better perspective on interlocked operations, try to learn how these are implemented in a cache-coherent CPU like Pentium in a multi CPU environment. One thing you would notice is, when you do interlocked operations the cache line would be invalidated and reloaded, so your L1, L2 caches are not used as much as they should be. Since memory access occurs in magnitudes of a cache line, data adjacent to your interlocked variable that are in the same cache line may be invalidated and reloaded from memory.


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