What is Memory Dependence Prediction? (Explained)

What is Memory Dependence Prediction?

Memory dependence prediction, also referred to as speculative load, indicates the process followed by the modern Processors or Central Processing Units to predict subsequent operations and executing the same out of order so as to enhance the overall performance.

This prediction typically depends on the accessing of the same data in the memory of the computer. This helps in avoiding an event of RAW or Read After Write dependency defiance.

KEY TAKEAWAYS

• Memory dependence prediction involves loads and stores and is related to branch predictions made for conditional instructions, but is just an improved version of it.
• Through this prediction, the CPU figures out whether two different memory operations are dependent or whether they do not interrelate by using the same memory location.
• The primary objective of such predictions is to avoid RAW events and dependence violations.
• By making a proper prediction, this process executes a few specific loads and stores out of order and at the same time keeps the others in order.
• If the predictions are made correctly then it reduces the time of operations and improves the overall performance of the processor and the computer system.

Understanding Memory Dependence Prediction

Memory dependence prediction, just as the name implies, is guessing the next operation by the processor so that it can avoid a RAW or Read After Write violation.

RAW violation happens when an attempt is made by the software to access the value of data that has been changed recently.

When this happens, the system has to follow a few specific functions, such as executing a pipeline flush.

This will eventually result in a delay in its performance.

Memory dependence prediction is very effective in the sense that it allows the CPU to issue load instructions early.

This process can prevent performance delays and RAW dependence violations from happening by accurately predicting the true reliance between loads and stores at the time of executing an instruction.

When the processor has the information about predicted dependence, it then becomes easy for it to decide to execute a few specific types of loads and stores speculatively out of order.

However, during this time, all the other loads and stores are kept in order.

If the prediction made is correct, then memory disambiguation methods are used in the pipeline later on to figure out whether or not the loads and stores were carried out properly. If not, these are recovered.

Typically, memory dependence prediction is an enhanced form of memory dependency speculation.

Normally, in the memory dependence speculation process, the load can be executed earlier than a store that is before it.

The speculation is correct and the load is executed successfully only when the load is not reliant on the store.

This means that the two instructions do not access the same memory locations.

On the other hand, a speculation is unsuccessful and the load is not executed correctly if the store and the load are dependent on each other.

This means that their accesses overlap in the memory.

However, memory speculation is not favored and is not used extensively because the benefits gained through this specific technique are pretty limited.

Therefore, a new type of prediction that is more aggressive, and, at the same time offers much higher benefits was needed.

This is offered by memory dependence prediction and it also helps in avoiding the costs of wrong speculations.

Typically, memory dependence prediction for loads and stores is analogous to branch prediction for conditional branch instructions.

The branch prediction technique follows two specific processes such as:

• First, the direction of the branch, taken or not, is determined by the predictor. This is normally called a binary decision.
• Second, the actual target address is determined by the predictor.

In the same way, memory dependence prediction is also a two-step process that involves the predictor determining the following:

• Whether or not there is a dependence
• If there is a dependence, then which it is.

There is a significant benefit to using memory dependence prediction. It reduces the number of flushes required in the pipeline to recover from the violations, which eventually augments its performance.

Therefore, memory dependence prediction is considered to be very important for designing the future processors.

If there is poor or no memory dependence prediction it will affect the IPC or Instructions Per Cycle of the future designs with severe constraints.

Memory Dependence Prediction Using Store Sets

Memory dependence prediction using store sets is a particular type of approach where those particular stores are first identified on which a specific type of load depends.

These stores are earmarked as ‘store sets,’ and this particular information is then communicated to the instruction scheduler. The necessary instructions are given accordingly.

Ideally, in order to maximize the performance of the CPU as well as the computer system as a whole, load instructions must be issued by an out-of-order processor as early as possible.

However, at the same time, it should be assured that the memory order with the previously stored instructions that write to the similar memory locations is not violated.

In order to do this, the CPU has to discover the correct load store set and then predict the earliest time as accurately as possible when the load can be executed safely.

This is a much better technique in comparison to an instruction scheduler which typically has just the right knowledge about memory dependency in advance.

It can be used in superscalar devices as well as in outsized instruction windows and get near-maximal performance.

The best part of predicting memory dependence using store sets is that it is quite a low-cost implementation to achieve a higher, if not the highest, performance.

Typically, the store set concept is based on two particular basic assumptions as follows:

• First, the history of the conduct of the memory order violation is considered which helps in predicting the future memory dependencies more correctly.
• Second, the prediction of dependencies among loads is very important when multiple loads are reliant on the same store or one load relies on several stores.

In such situations, it is found that the performance of the infinite memory dependence prediction can be maintained by applying a few precincts to the store sets.

This is because the hardware that is used to put the store sets into practice typically consists of structures that have fixed dimensions.

When the size of these structures is restricted, it will force all disparate loads to share the store sets.

It is highly likely that such a move will result in false dependencies.

In order to prevent this from happening and also ensure that the benefits of idealized store sets are retained, a set of rules is required to be created and used for store set assignment.

This process also helps in creating an inexpensive solution, which is mainly due to the direct mapped structures.

These structures help in limiting a lot of things such as:

• The total number of loads that can have store sets of their own
• The stores to be in one store at the most at one time
• The stores execute in order in a store set.

This will allow for more dynamic store set merging, thereby enabling multiple loads to rely on the same store.

Using the theory of store sets as the foundation for memory dependence prediction will typically idealize the structure yields ensuring a near-optimal prediction accuracy.

Conclusion

Memory dependence prediction is a useful technique that reduces the time taken by the CPU to execute instructions by reducing the number of flushes in the pipeline due to wrong loads and stores operations.

This not only lowers the need to recover due to RAW violations but also improves the system performance.