wiki:DataParallel/Benchmarks

Version 5 (modified by chak, 6 years ago) (diff)

--

First NDP benchmarks (2007)

Sparse matrix vector multiplication

This benchmark is explained it much detail in Data Parallel Haskell: a status report. Runtimes comparing to sequential C code on Intel Xeon (x86) and Sun SunFire9600 (Sparc) are in time-colour.png. The parallel Haskell code is more efficient from 2 PEs for the SunFire and from 4 PEs for the Xeon processors. We blame the low sequential performance for the Xeon on the lack of effort that has been put into generating good straight-line code (both in the NCG and when compiling via C), this includes inadequate register allocation and lack of low-level optimisations.

The speedup for the Xeon box and the SunFire are in speedup-colour.png and the speedup for our 8x dualcore Opteron NUMA box is in serenity-all-speedup-colour.png. The speedup of on the NUMA machine is limited by the memory bandwidth for smvm. When we only use one core per CPU, the benchmark scales much better. Moreover, the memory traffic/compute ratio is slightly more favourable when processing arrays of Floats than when processing arrays of Doubles.

Connected components in undirected graphs

This is based on http://www.cs.cmu.edu/~scandal/nesl/algorithms.html#concomp. Currently, two of the tree algorithms described there are implemented: Awerbuch-Shiloach and the hybrid algorithm. The random mate algorithm needs a data-parallel random number genenerator and is left for later.

The algorithms are interesting because they actually don't do a lot of computations; they mostly filter and copy edges. Thus, it is perhaps not unreasonable to expect that if they scale well, then so will more computation-intensive ones. Also, they should make any inefficiencies introduced by missed fusion opportunities quite obvious.

Both algorithms take the number of nodes (n :: Int) and an array of edges (es :: Int :*: Int) and yield an array of Ints of length n where each node is assigned a number between 0 and n-1. Nodes which are assigned the same number are connected. The algorithms are described and compared in http://citeseer.ifi.unizh.ch/greiner94comparison.html. At the moment, I have only benchmarked them for a random graph with 1000000 nodes and 40000 edges; eventually, I'll add benchmarks for other kinds of graphs described in the paper. The benchmarks have been run on a dual Intel Xeon 2.8 GHz with two cores per processor which effectively gives us 4 processors overall.

The parallel versions are currently very much slower than the sequential ones, particularly so for Awerbuch-Shiloach. This is because fusion doesn't work for the parallel algorithms at the moment.

No benchmarks on 4 processors yet, as the 4th PE is currently busy.

Awerbuch-Shiloach

The sequential code is in http://darcs.haskell.org/packages/ndp/Data/Array/Parallel/test/nesl/concomp/AwShU.hs and the parallel in http://darcs.haskell.org/packages/ndp/Data/Array/Parallel/test/nesl/concomp/AwShUP.hs.

Version Threads Time (ms) Speedup
sequential 1600
parallel 1 29800
2 16800 1.8
3 12800 2.3
4 ??? ???

Hybrid

The sequential code is in http://darcs.haskell.org/packages/ndp/Data/Array/Parallel/test/nesl/concomp/HybU.hs and the parallel in http://darcs.haskell.org/packages/ndp/Data/Array/Parallel/test/nesl/concomp/HybUP.hs.

Version Threads Time (ms) Speedup
sequential 1850
parallel 1 7450
2 4600 1.6
3 3800 2.0
4 ??? ???

I haven't completely parallelised this one yet (it's only a matter of implementing some parallel combinators).

Attachments (3)

Download all attachments as: .zip