In a multi-threaded environment, the lock-free algorithms provide a way in which threads can access the shared resources without the complexity of Locks and without blocking the threads forever. These algorithms become a programmer’s choice as they provide higher throughput and prevent deadlocks.
This is mainly because designing lock-based algorithms, for concurrency brings its own challenges on the table. The complexity of writing efficient locks and synchronization to reduce thread contention is not everyone’s cup of tea. And besides, even after writing the complex code, many times, hard-to-find bugs occur in the production environments, where multiple threads are involved, which becomes even more difficult to resolve.
Keeping this perspective, we will be talking about how we can apply a lock-free algorithm to one of the widely used data structures in Java, called the Stack. As we know Stack is used in many real-life applications like Undo/redo functions in a Word processor, Expression evaluation and syntax parsing, in Language Processing, in supporting recursions and also our own JVM is Stack oriented. So, let’s have some insight into how to write a lock-free stack. Hope it ignites your mind enough to read and gain knowledge on this topic, further.
Atomic Classes in Java
Java provides a plethora of classes that support lock-free and thread-safe programming. The Atomic API provided by Java, java.util.concurrent.atomic package contains many advanced classes and features which provide concurrency control without having to use locks. The AtomicReference is also one such class in the API which provides a reference to the underlying object references that can be read and written atomically. By atomic, we mean the reads from and writes to these variables are thread-safe. Please refer to the below link for details.
CAS Inside – CompareAndSwap Operation:
The most important operation which is the basic building block for the lock-free algorithms is the compare and swap. It compiles into a single hardware operation, which makes it faster as the synchronization appears on a granular level. Also, this operation is available in all the Atomic Classes. CAS aims at updating the value of a variable/reference by comparing it with its current value.
Applying CAS for a Non-Blocking Stack:
A non-blocking stack basically means that the operations of the stack are available for all the threads and no thread is blocked. To use CAS in the stack operations, a loop is written wherein the value of the top node (called stack top) of the stack is checked using CAS. If the value of stackTop is as expected, it is replaced with the new top value, else nothing is changed and the thread goes into the loop again.
Let’s say we have an Integer Stack. Suppose, thread1 wants to push a value 77 on to the stack when the top of the stack value is 90. And thread2 wants to pop the top of the stack which is 90, currently. If thread1 tries to access the Stack and is granted access because no other thread is accessing it at that time, then the thread first gets the latest value of stack top. Then it enters the CAS loop and checks the stack top with the expected value (90). If the two values are the same, ie: CAS returned true, which means no other thread has modified it, the new value (77 in our case) is pushed on to the stack. And 77 becomes the new stack top. Meanwhile, thread2 keeps looping the CAS, until CAS returns true, for popping an item from the top of the stack. This is pictured below in the diagram.
Code Example for the Non-Blocking Stack :
The Stack code sample is shown below. In this example, there are two stacks defined. One which uses traditional synchronization(named ClassicStack here) to achieve concurrency control. The other stack uses the compare-and-set operation of AtomicReference class for establishing a lock-free algorithm(named as LockFreeStack here). Here we are counting the number of operations performed by the Stack in a span of 1/2 a second. We compare the performance of the two stacks below :
Pushing into stack... Pushing into stack... Popping from stack ... Popping from stack ... The number of stack operations performed in 1/2 a second-->28514750
The above output is received from implementing the Lock Free stack data structure. We see there are 4 different threads, 2 for pushing and 2 for popping from Stack. The number of operations means either Pop or Push operations on the stack.
To compare it with the standard stack version where traditional synchronization is used for concurrency, we can just uncomment the first line of the code and comment on the second line of code as follows.
The output from the lock-based stack is below. It clearly shows that the lock-free implementation(above) provides almost 3 times more output.
Pushing into stack... Pushing into stack... Popping from stack ... Popping from stack ... The number of stack operations performed in 1/2 a second-->8055597
Though lock-free programming offers a myriad of benefits, programming it correctly is no trivial task.
- Truly Lock-free programming.
- Deadlock Prevention.
- Higher Throughput.
- The A-B-A problem can still happen in a lock-free algorithm(which is a change in the value of a variable from A to B then back to A while two threads are reading the same value A, without the other thread knowing it)
- Lock-free algorithms may not always be very easy to code.
Lock-free algorithms and data structures is a much-debated topic in the Java World. When using lock-based or lock-free algorithms, a thorough understanding of the system must be done. One must be very mindful to use either of them. There’s no “one size fits all” solution or algorithm for different types of concurrency problems. So, deciding what algorithm suits best in a situation, is a crucial part of programming in the Multi-threaded World.
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