Sorting out graph processing

https://github.com/frankmcsherry/blog/blob/master/posts/2015-08-15.md

by mrry • 11 years ago

107 11 11 years ago

anonetal• 11 years ago

As Frank notes, this is not really surprising if you have seen the recent-ish papers on sorting. It is indeed frustrating when a paper seems to have nice ideas but does not do a good enough job at the simple baselines; and in most cases, there is no way for you to easily check your intuitions :(

amirouche• 11 years ago

why do you think is difficult to check your intuitions?

jules• 11 years ago

Brilliant writing style! Also check out his work on Naiad, which is IMO by far the most interesting work in its area, and check out the Scalability! But at what COST? paper: http://www.frankmcsherry.org/assets/COST.pdf which showed that a program running on a single thread often outperforms distributed systems running on 100+ cores.

lorenzhs• 11 years ago

Random memory accesses are very expensive. This is because TLB misses are typically resolved using a tree data structure (a hash table is not a good choice as knowledge of the hash function would allow for timing attacks). This tree has logarithmic depth (although with a large base). Huge pages make this a lot cheaper, but if they are not used, you might well end up with a situation where n accesses to random positions in a very large array of size n are similarly expensive to sorting the array.

ibdknox• 11 years ago

Looks like one of the x-stream guys replied: https://github.com/ar104/sortingVsScanning/blob/master/Sorti...

Related discussion on twitter: https://twitter.com/roy_amitabha/status/634078514698952704

yzh• 11 years ago

I'm doing GPU graph processing: http://gunrock.github.io I think another critical aspect of getting high performance graph processing is load balancing. Also, there are tons of papers talking about how to reorganize data to get coalesced memory access. Combining the above strategies can outperform pre-processing such as sorting. At least on GPU this holds.

qqueue• 11 years ago

The downside to radix sort is that is just looks at bytes. If you had some deep and semantically meaningful ordering defined over your type, (or, like, a pointer) that's great but we're sorting by its bytes.

See Fritz Henglein's "Generic Top-down Discrimination for Sorting and Partitioning in Linear Time" paper for a nice way to extend radix sort's runtime to arbitrary meaningful orderings:

http://www.diku.dk/hjemmesider/ansatte/henglein/papers/hengl...

Beginning of a haskell implementation here: http://ekmett.github.io/discrimination/index.html . Might be doable in rust as well.

elliptic• 11 years ago

Is this claim correct? "You do four of these sequential scans, and in each you write sequentially to one of 256 locations. Four passes, no random access. This is great. I don't even see a log n there, do you?"

You're writing to one of 256 (presumably far apart) memory locations - why isn't this considered random access?

frankmcsherry2• 11 years ago

The cost of writing to some number of unrelated locations is mostly determined by the level of cache where your working set can stay resident. You'll be doing random access to that level of cache, but it can exchange data with the next level cache in larger contiguous blocks.

In this case, 256 cache lines fit in the L1 cache, so while it does look like "random access" to the L1, this can end up looking more like "sequential access" to main memory. There are addition complications, like the L1 data cache being only 8-way associative, a quite small TLB cache sitting in front of the L2 and up, etc. So in practice this ends up somewhere between random access to L1 and random access to L3, with sequential access to main memory, I think.

asdfjghafsdkh• 11 years ago

For large MSB radix sorts you should do 6 bits at a time (64 bins) to avoid spilling the TLB cache. You can switch to 8 bits when you get below 64 pages (256k).

Also, for sorting 64-bit integers I like to do a bitwise or of all the integers while computing the histogram. Then if the bits you are looking at are zero (plausible for unsigned 64-bit integers) you can skip to the ones that matter for the next histogram.

frankmcsherry2• 11 years ago

There is this cool related work [0] that I wanted to talk about, but couldn't get to show an improvement on my laptop, which uses software write combining to mitigate cache limitations. In particular, they are worried about the fact that 8-way associative L1 doesn't like to hold 256 specific cache lines, so they manually buffer writes into 16k (= 256 cache lines) contiguous bytes, using a non-temporal write when a cache line is full. They report hitting 88% of their system's peak memory bandwidth.

You could do the same thing (I think) to avoid TLB limitations, manually staging everything in a contiguous 2MB of memory (backed by one large page, say), in order to keep the radix high and do fewer full scans. If you are hitting memory bandwidth, your performance should be determined by the number of scans you end up doing.

All of this is "caveat: I just read other people's work and haven't done this myself, because I haven't figured out inline asm in Rust yet". If you have more details on engineering radix sort, I'd love to read up! :D

[0] http://arxiv.org/abs/1008.2849

Sorting out graph processing

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