How can instruction fetch and decode pipeline stages run simultaneously in a CPU with dynamic branch prediction?

I have recently been investigating CPU pipelining and branch prediction and have a question about how exactly these fit together.

If, for example, instructions are meant to be fetched in one stage of the pipeline and decoded in the next while the next instruction is fetched simultaneously, how is it possible for the pipeline to proceed without a stall when dynamic branch prediction is in operation?

As an instruction must be decoded before branch prediction can occur or be deemed unneeded, and as any prediction must be made before the next instruction can be fetched, how can an instruction be decoded while the next instruction is fetched in the same clock cycle?

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Which time series prediction techniques are useful given harmonic properties?

I have a time series dataset where events have harmonic properties, and seemingly the nature of the event’s early segments can determine the remainder of the event (see example 1’s oscillations). Additionally, these early segments might determine the properties/likelihood of some following/associated event (example 2).

Which time series prediction techniques may be suitable for making predictions based on this kind of behaviour? Specifically:

  1. For determining how an event, from appearance, might oscillate and dissipate (forecast/extrapolation).
  2. For predicting what event may follow (probabilistic model).

I am not very knowledgeable in this, but seemingly dynamic ARIMA may be appropriate for the extrapolation at a given point. Also, the early segment might be suitable as a feature for prediction in some other model (e.g. SVC)?

Example Event 1


Example Event 2


How client movement prediction syncs with server position to avoid a clip around collision?

I have searched a lot on stackexchange and on google, yet I have not found a satisfactory answer to this seemingly simple question.

I am making an online multiplayer game where the players navigate a 2d map with basic square obstacles. In this case I am making collision checks both on the client and the server, where the server broadcast everyone’s position once per second as authority. However, when making a sharp turn around a square, often times either the client makes the turn while the server clips, or the client clips at the corner while the server makes the turn. That is understandable considering the possible discrepancy between the position on the server and the predictive position of the client. How do online multiplayer games usually work around this simple yet crucial problem?

I understand that it comes down to a pixel perfect sync which isn’t possible, but how do other games navigate around this issue, to give a flawless collision experience?

What I am considering of doing:

  1. Increase the position broadcasting rate from 1 time per second to however high needed to accurately sync the positions (20/sec). I believe this is the solution most games are doing. This however, will drastically increases the load on the server.
  2. In my case, the game’s main mechanism isn’t the navigation (think MMO), so I am leaning towards only checking collision on the client side, and have a simpler collision check on the server to ban walk-through-wall hackers. This would relax the server from heavy collision operations which is nice too.

Any CPUs using value prediction, dynamic instruction reuse?

There is a lot of research about techniques that try to reuse the previous result of an instruction, either memory loads or arithmetic, such as dynamic instruction reuse, value prediction, based on the concept of value locality.

What I wonder if there is any commercial CPU from Intel/AMD/ARM that actually uses any of these techniques, or are they still far away from being implemented in CPUs?

I mostly see these techniques as the runtime, CPU version of compiler optimizations such as common-subexpression elimination, loop invariant code motion, redundant load removal, catching cases that cannot be handled due to pointer aliasing, side-effects, etc.

What is the right term/theory for prediction of Binary Variables based upon their continuous value?

I am working with a linear programming problem in which we have around 3500 binary variables. Usually IBM’s Cplex takes around 72 hours to get an objective with a gap of around 15-20% with best bound.In the solution, we get around 85-90 binaries which have value of 1 and others are zero. The objective value is around 20 to 30 million. I have created an algorithm in which I am predicting (fixing their values) 35 binaries (with the value of 1) and letting the remaining ones solved through the Cplex. This has reduced the time to get the same objective to around 24 hours (the best bound is slightly compromised). I have tested this approach with the other (same type of problems) and it worked with them also. I call this approach as “Probabilistic Prediction”, but I don’t know what is the standard term for it in mathematics?

Below is the algorithm:

Let y=ContinousObjective(AllBinariesSet); WriteValuesOfTheContinousSolution(); Let count=0;  Let processedbinaries= EmptySet; while (count < 35 ) { Let maxBinary =AllBinariesSet.ExceptWith(processedJourneys).Max();//Having Maximum Value between 0 & 1 (usually lesser than 0.6)             processedJourneys.Add(maxBinary); maxBinary=1; Let z = y; y = ContinousObjective(AllBinariesSet); if (z > y + 50000)                  { //Reset maxBinary maxBinary.LowerBound = 0; maxBinary.UpperBound = 1; y = z; } else { WriteValuesOfTheContinousSolution(); count=count+1; }              } 

According to me, it’s working because the solution matrix is very sparse and there are too many good solutions.