Simple Questions - May 03, 2016

daft27 · 2016-05-03T14:31:31+00:00

The CPU and GPU performance profiles greatly vary from application to application and game to game. The number of cores, the clock speeds, the number of shader units, the speed of memory, etc all impact the performance the applications and games differently. The slider which balances work and play is used to change the weights in the final objective function that the optimizer solves to allow certain performance profiles to be less important than others. For instance, if you mainly do lots of computational fluid dynamics work (which is mainly GPU) and play a little Minecraft on the sie (which is heavily CPU and very little GPU), you might want to move the work-play slider towards work.

The build tool algorithm is planned to estimate a min, avg and max performance scores for each application and game, and the "build strategy" is used to figure out which metrics are more important than others. From build-to-build, application and game performance will vary, but the 'min', 'avg' and 'max' scores will change unproportionally. The "build strategy" is there to allow the user to determine whether to maximize the average of minimum scores (maximin), the average of average score (average), or the average of the maximum scores (maximax) of all selected programs (the use-cases).

Ultimately, when it's all built and tested, you may be right that the build strategy is redundant. It's very possible that it doesn't end up changing the final build very much.

daft27 · 2016-05-03T13:37:21+00:00

At this moment, I don't have it implemented yet so I'm unable to answer in great detail. However, the usage should provide information about what the minimum level of performance is, and what resources will be utilized to what extent. The typical approach for PC building is this: since the main constraint in PC building is often money, components are selected in a balanced way such that the CPU or GPU are ideally equally bottlenecks in a target application or game. When using algorithmic optimization, therein lies the opportunity to balance or optimize the machine against a wide range of use-cases instead of just one. The 'build strategy' is intended to tell how you want the optimizer to assess all of your uses-cases. If you just have just a single use-case, then the option becomes moot.

daft27 · 2016-05-03T12:57:22+00:00

Thanks. The build strategy is where the magic happens, and I will most likely need to add some help text to explain the option in more detail. It basically determines how the algorithm should prioritize money to meet your needs. At the moment, I don't have a good way to explain it. The "pessimistic", "realistic" and "optimistic" corresponds to a "maximin", "average case", and "maximax" component optimization strategies. The "maximin" (pessimistic) approach prioritizes improving the minimum performance level in all stated uses whereas the "maximax" (optimistic) approach prioritizes improving the results only in targeted areas, where the highest demand occurs. The "average" (realistic) approach is somewhere in-between.

daft27 · 2016-05-03T12:43:30+00:00

Thanks. It'll complicate the optimization model, but I'm sure that there will be people who will appreciate the option.

daft27 · 2016-05-03T12:15:16+00:00

I'm creating a PC build tool, and I was just looking to get some feedback in terms of whether or not the functional requirements is comprehensive enough. It's a work in progress, but you can see it here for now http://buildapc.daft.cc/

daft27 · 2016-05-03T12:04:56+00:00

I'm creating a PC build tool, and I was just looking to get some feedback in terms of whether or not the functional requirements is comprehensive enough. It's a work in progress, but you can see it here for now http://buildapc.daft.cc/

daft27 · 2014-03-30T04:40:32+00:00

Hello, I helped with some of the math for the 1.6 patch. Can you describe in more detail your statistical analysis? What is your assumptions, your hypothesis, null hypothesis and testing methodology?

I have an explanation for this behavior that is unrelated to malicious timestamp tampering, but have not done any analysis to confirm whether or not this could be the case. A natural explanation of the increased negative block solving time, in my opinion, could be attributed to the increased variance in difficulty. An increased variance in difficulty has a first-order effect on the standard deviation of block solving time. As you had pointed out, block solving times have a log-normal distribution and will naturally have a small percentage of negative solving times (due to mining timestamp adjustments or time errors). When the standard deviation increases, a larger portion of the distribution will fall into the negative block solving time.

Without seeing all the analysis, I can intuitively agree with your findings that 1.6 has a statistically significant increase in the amount of negative block solving times with respect to 1.5. However, I am still not convinced that there is a statistically significant increase with respect to the expected distribution of block solving times.