|Version 11 (modified by chak, 7 years ago) (diff)|
Status of DPH Benchmarks
This page gives an overview of how well the benchmarks in the examples/ directory of package dph are currently working.
Overview over the benchmark programs
- Computes the dot product of two vectors of Doubles. There are two variants of this program: (1) "primitives" is directly coded against the array primitives from package dph and (2) "vectorised" is a high-level DPH program transformed by GHC's vectoriser.
- Multiplies a dense vector with a sparse matrix represented in the compressed sparse row format (CSR). There are three variants of this program: (1) "primitives" is directly coded against the array primitives from package dph and (2) "vectorised" is a high-level DPH program transformed by GHC's vectoriser.
Execution on LimitingFactor (2x Quad-Core Xeon)
Hardware spec: 2x 3.0GHz Quad-Core Intel Xeon 5400; 12MB (2x6MB) on-die L2 cache per processor; independent 1.6GHz frontside bus per processor; 800MHz DDR2; 256-bit-wide memory architecture; Mac OS X Server 10.5.6
Software spec: GHC 6.11 (from end of Feb 09); gcc 4.0.1
|Program||Problem size||sequential||1 core||2 cores||4 cores||8 cores|
|DotP, primitives||100M elements||823/823/824||812/813/815||408/408/409||220/223/227||210/214/221|
|DotP, vectorised||100M elements||823/824/824||814/816/818||412/417/421||222/225/227||227/232/238|
|DotP, ref Haskell||100M elements||–||810||601||221||209|
|SMVM, primitives||?? elems, density ??|
|SMVM, vectorised||?? elems, density ??|
All results are in milliseconds, and the triples report best/average/worst execution case time (wall clock) of three runs. The column marked "sequential" reports times when linked against dph-seq and the columns marked "N cores" report times when linked against dph-par and run in parallel on the specified number of processor cores.
NB: At the moment, the CPU times goes up proportional to the number of cores added beyond four cores (hence, no wall clock speed up beyond four cores). Not clear where this comes from.
|Program||Sequential (manually vectorised)||Vectorised||Parallel|
|DotP||Order of mag. faster than list impl||Same performance as seq.||speedup of 2 for 2 CPUs, 4 threads|
|QuickSort||Slower than list (fusion)||Slower than seq. (why?)||speedup of 1.4 on 2 CPUs|
|SparseVector||Similar to DotP|
|Primes (Nesl)||15 x faster than list version||NYI||20 x slower than seq (fusion?)|
|BarnesHut||Small bug in alg||Working||See seq.|
- I only ran a first set of benchmarks when checking for what's there. I'll run the benchmarks properly as next step
- Fusion doesn't work well on parallel programs yet, so for all but simple examples, the parallel program performs worse than the sequential
- The compiler doesn't exploit all fusion opportunities for QSort and BarnesHut. Once this is fixed, they should run considerably faster.
- Interestingly, the automatically vectorised version of qsort is quite a bit faster than the hand-flattened. Need to find out why.