The Parallelism Blues: when faster code is slower | Hacker News Reader

The Parallelism Blues: when faster code is slower

pythonspeed.com

36 pointsbibyte23 comments

In Julia, where the paralleization options are explicit (SIMD, AVX, threads or multiprocessing), it always depends on the load, for small operation (around 10000 elements) a single thread is faster only for the thread spawning time (around 1 microsecond). And there is the issue of the independent Blas threaded model, where the Blas threads sometimes interfere with Julia threads... In a nutshell, parallelization is not a magical bullet, but is a good bullet to have at your disposal anyway

Do you know if Julia will add OpenMP support? It's clearly the way to go for offloading to hardware in a productive way.

I don't know about "clearly the way to go". I think Julia's parallelism models have proven themselves to be very robust, performant and composeable, moreso than OpenMP as far as I'm aware.

How can I annotate an existing loop to offload it on the GPU Inclusive OR on AVX IOR on cpu cores. Without this ability, in practice I use far less parallelism.

This is currently no official solution in Julia that I'm aware of. However there are several people working on it and a few experimental solution are under active development

https://github.com/JuliaDiffEq/AutoOffload.jl

https://juliagpu.gitlab.io/GPUifyLoops.jl/

AutoOffload is something different, where it's trying to do linear algebra in a way that auto-offloads to GPUs or heterogeneous. GPUifyLoops is correct for this answer, and its next incarnation is KernelAbstractions.jl. These auto-construct GPU kernels and such from loops.

Julia is actually initially had OpenMP backed parallelism (ParallelAccelerator.jl), but they're moving away from OpenMP towards a novel and native task parallelism framework more inspired by things like Cilk[0].

[0] https://julialang.org/blog/2019/07/multithreading/

> And there is the issue of the independent Blas threaded model, where the Blas threads sometimes interfere with Julia threads

Julia has composible multithreading, and using that model fixed composing FFTW threads with Julia's. This can be done to OpenBLAS as well, and IIRC there is a PR open for it.

Yeah, I'm waiting for that PR haahah

There's also the problem of Turbo Boost.

My laptop's 9980HK will boost to ~4.5 GHz when only loaded to a single core.

However, when I load up all 8 cores, it might only sustain ~3.5 GHz.

Therefore the 8 cores might not actually result in the work being completed 8 times as fast, only 6.2x (8*[3.5/4.5]) real-time due to the lowered clock rate of each individual core.

This will show up as additional user time, since each individual core is able to do less work for each unit of time (seconds) compared to the single-core case.

"It would be extremely surprising, then, if running with N threads actually gave ×N performance."

Basically impossible by Ahmdal's law.

well before that kicks in, if your code requires any coordination at all (not Monte Carlo) then those overheads can scale with the number of processes. so rather than hitting an asymptote as you'd get with just Ahmdal, you actually start to go down in absolute terms as the number of processes increases.

Of course in the general case Amdahl's law is inescapable, but some tasks on modern systems can show > ×N speedup over single-threaded performance if, for example, a single thread can only exploit at maximum some fraction of the total memory bandwidth or some level of the cache hierarchy.

Do you have examples where running n threads instead of 1 results in a speedup greater than n?

The only thing I can think of would be that the additional threads would kick the CPU into using a higher frequency, but a single thread using 100% of the CPU should already do that.

There are some broad examples of that effect here: https://en.wikipedia.org/wiki/Speedup#Super-linear_speedup

The caching effects mentioned there have to do with adding additional hardware resources that change the effective cache sizes and shift the working set into higher performing storage or memory.

You can also encounter a funny software engineering effect. Refactoring an array-based algorithm to work in parallel often involves the introduction of block-decomposition. This change allows sub-problems to be spread to different workers. Sometimes, this change will also accelerate the sequential code, as the new block-oriented access patterns improve cache efficiency!

Oh I see, that makes complete sense.

Basically, given two small arrays A and B (such that both fit in the CPU caches), if the computations to do are sum(A) * sum(B) and sum(B) / sum(A), having two threads that do the computations will be more than twice as fast as a single thread. In the two thread case, A and B will be fetched in parallel (assuming left-to-right evaluation), incurring only one round of pipeline stalls for data fetching, whereas the single threaded case will have to incur two pipeline stalls from memory fetching.

None of this is surprising, right? Unless your system has fewer threads than cores (which it probably doesn't even without your program) there will always be some context-switching overhead. It's worth keeping in mind I guess - especially the fact that numpy parallelizes transparently - but generally these results are to be expected.

The title is also misleading; it suggests that the wall clock time might be longer for parallel code in certain cases. While not impossible, that isn't what the article covers.

> the wall clock time might be longer for parallel code

That is exactly the case, if CPU is the bottleneck in your already-parallel application. It's a case where we really shouldn't be layering different parallel bits together in one codebase, but might be doing it naively.

The article uses the term "parallelism" when it is talking, instead, about concurrency.

Parallelism is specifically the stuff that actually does happen completely independently on all processing units, that actually goes Nx as fast on N units (clock depression aside). Concurrency refers to the overhead of coordinating activity of those units, that keeps you from getting your Nx. It is overhead on top of any actually serial parts of the computation, which Amdahl's law addresses.

In other words: Parallelism giveth, and concurrency taketh away.

The distinction gets more useful the more you think about the subject.

On 64 core Xeon E5 this example gives 1.8x increase in user time, but 8x decrease in wall-clock time

This is perfectly normal behavior when Intel Hyperthreading is involved.

I'm on my phone, so rather than trying to type out an explanation, I'm going to link to Wikipedia: https://en.wikipedia.org/wiki/Hyper-threading

The benchmarks were run on a computer with hyperthreading disabled.

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