Misses | 仿写百思不得姐 React Native | Frontend Framework library

Looking at the result of Cachegrind, it doesn't look like the memory is your bottleneck. The NN has to be stored in memory anyway, but if it's too large that your program's having a lot of L1 cache misses, then it's worth thinking to try to minimize L1 misses, but 1.7% of L1 (data) miss rate is not a problem.

So you're trying to make this run fast anyway. Looking at your code, what's happening at the most inner loop is very simple (load-> multiply -> add -> store), and it doesn't have any side effect other than the final store. This kind of code is easily parallelizable, for example, by multithreading or vectorizing. I think you'll know how to make this run in multiple threads seeing that you can write code with some complexity, and you asked in comments how to manually vectorize the code.

I will explain that part, but one thing to bear in mind is that once you choose to manually vectorize the code, it will often be tied to certain CPU architectures. Let's not consider non-AMD64 compatible CPUs like ARM. Still, you have the option of MMX, SSE, AVX, and AVX512 to choose as an extension for vectorized computation, and each extension has multiple versions. If you want maximum portability, SSE2 is a reasonable choice. SSE2 appeared with Pentium 4, and it supports 128-bit vectors. For this post I'll use AVX2, which supports 128-bit and 256-bit vectors. It runs fine on your CPU, and has reasonable portability these days, supported from Haswell (2013) and Excavator (2015).

The pattern you're using in the inner loop is called FMA (fused multiply and add). AVX2 has an instruction for this. Have a look at this function and the compiled output.

Yes, data misalignment could explain your 2x slowdown for small arrays that fit in L1d. You'd hope that with every other load/store being a cache-line split, it might only slow down by a factor of 1.5x, not 2, if a split load or store cost 2 accesses to L1d instead of 1.

But it has extra effects like replays of uops dependent on the load result that apparently account for the rest of the problem, either making out-of-order exec less able to overlap work and hide latency, or directly running into bottlenecks like "split registers".

ld_blocks.no_sr counts number of times cache-line split loads are temporarily blocked because all resources for handling the split accesses are in use.

When a load execution unit detects that the load splits across a cache line, it has to save the first part somewhere (apparently in a "split register") and then access the 2nd cache line. On Intel SnB-family CPUs like yours, this 2nd access doesn't require the RS to dispatch the load uop to the port again; the load execution unit just does it a few cycles later. (But presumably can't accept another load in the same cycle as that 2nd access.)

• https://chat.stackoverflow.com/transcript/message/48426108#48426108 - uops waiting for the result of a cache-split load will get replayed.
• Are load ops deallocated from the RS when they dispatch, complete or some other time? But the load itself can leave the RS earlier.
• How can I accurately benchmark unaligned access speed on x86_64? general stuff on split load penalties.

The extra latency of split loads, and also the potential replays of uops waiting for those loads results, is another factor, but those are also fairly direct consequences of misaligned loads. Lots of counts for ld_blocks.no_sr tells you that the CPU actually ran out of split registers and could otherwise be doing more work, but had to stall because of the unaligned load itself, not just other effects.

You could also look for the front-end stalling due to the ROB or RS being full, if you want to investigate the details, but not being able to execute split loads will make that happen more. So probably all the back-end stalling is a consequence of the unaligned loads (and maybe stores if commit from store buffer to L1d is also a bottleneck.)

On a 100KB I reproduce the issue: 1075ns vs 1412ns. On 1 MB I don't think I see it.

Data alignment doesn't normally make that much difference for large arrays (except with 512-bit vectors). With a cache line (2x YMM vectors) arriving less frequently, the back-end has time to work through the extra overhead of unaligned loads / stores and still keep up. HW prefetch does a good enough job that it can still max out the per-core L3 bandwidth. Seeing a smaller effect for a size that fits in L2 but not L1d (like 100kiB) is expected.

Of course, most kinds of execution bottlenecks would show similar effects, even something as simple as un-optimized code that does some extra store/reloads for each vector of array data. So this alone doesn't prove that it was misalignment causing the slowdowns for small sizes that do fit in L1d, like your 10 KiB. But that's clearly the most sensible conclusion.

Code alignment or other front-end bottlenecks seem not to be the problem; most of your uops are coming from the DSB, according to idq.dsb_uops. (A significant number aren't, but not a big percentage difference between slow vs. fast.)

How can I mitigate the impact of the Intel jcc erratum on gcc? can be important on Skylake-derived microarchitectures like yours; it's even possible that's why your idq.dsb_uops isn't closer to your uops_issued.any.

The front-end response will be in that form that you'll set it there. Then the ASP middleware will parse all those strings back to an object at your back-end.

So key moments here are:

• a controller action parameter type - it could be any of your types but it should correlate with your front-end;
• front-end's select element name attribute value - it should contain full existing path.

As I got from your code example the following.

1. You have DummyViewModel view model class. It has property Dummies.
2. You have Dummy class, that nested in DummyViewModel as Dummies. 2nd level dictionary.
3. You have DummyEnum enum class, that is in use at DummyEnum values. Same names, different adjacent levels.
4. The SelectList values are OK. They are directly from the enum.
5. Based on the structure, to set up a first enum value you need to navigate its level by setting KEY and VALUE. Then do it again for another level. For me, the first enum value in this structure should have something like this:

Here is a tr + sed solution that should work on any POSIX shell as it doesn't call any gnu utility:

For a while I was looking for a similar problem in reviews of popular live wallpapers. I found similar reviews only for one application. I updated my phone today. All my live wallpapers stopped working as expected. Then I installed "Earth & Moon" and this app works fine. It means that we are doing something wrong or, on the contrary, we are not doing something :) In the near future I will begin to investigate this problem.

First off, you can further reduce the theoretical cycles to 6 by replacing the first mul with uzp1 and doing the following smull and smlal the other way around: mul, mul, smull, smlal => smull, uzp1, mul, smlal This also heavily reduces the register pressure so that we can do an even deeper unrolling (up to 32 per iteration)

And you don't need v2 coefficents, but you can pack them to the higher part of v1

Let's rule out everything by unrolling this deep and writing it in assembly:

Create another group with rleid (from data.table) and paste the rows in summarise

The TLDR is that loads which miss all levels of the TLB (and so require a page walk) and which are separated by address unknown stores can't execute in parallel, i.e., the loads are serialized and the memory level parallelism (MLP) factor is capped at 1. Effectively, the stores fence the loads, much as lfence would.

The slow version of your insert function results in this scenario, while the other two don't (the store address is known). For large region sizes the memory access pattern dominates, and the performance is almost directly related to the MLP: the fast versions can overlap load misses and get an MLP of about 3, resulting in a 3x speedup (and the narrower reproduction case we discuss below can show more than a 10x difference on Skylake).

The underlying reason seems to be that the Skylake processor tries to maintain page-table coherence, which is not required by the specification but can work around bugs in software.

For those who are interested, we'll dig into the details of what's going on.

I could reproduce the problem immediately on my Skylake i7-6700HQ machine, and by stripping out extraneous parts we can reduce the original hash insert benchmark to this simple loop, which exhibits the same issue: