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

 SumSq

Computes the sum of the squares from 1 to N using Int. 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. As a reference implementation, we have a sequential C program denoted by "ref C".

 DotP

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. In addition to these two DPH variants of the dot product, we also have two non-DPH reference implementations: (a) "ref Haskell" is a Haskell program using imperative, unboxed arrays and and (b) "ref C" is a C implementation using pthreads.

 SMVM

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. As a reference implementation, we have a sequential C program denoted by "ref C".

 Primes

The Sieve of Eratosthenes using parallel writes into a sieve structure represented as an array of Bools. We currently don't have a proper parallel implementation of this benchmark, as we are missing a parallel version of default backpermute. The problem is that we need to make the representation of parallel arrays of Bool dependent on whether the hardware supports atomic writes of bytes. Investigate whether any of the architectures relevant for DPH actually do have trouble with atomic writes of bytes (aka Word8).

 Quickhull

Given a set of points (in a plane), compute the sequence of points that encloses all points in the set. There is only a vectorised version. Currently doesn't work due to bugs in dph-par. Also needs to get a wrapper using the new benchmark framework to generated test input and time execution.

 Quicksort

FIXME

 ConComp

Implementation of the Awerbuch-Shiloach and Hybrid algorithms for finding connected components in undirected graphs. There is only a version directly coded against the array primitives. Needs to be adapted to new benchmark framework.

 BarnesHut

This benchmark implements the Barnes-Hut algorithm to solve the n-body problem in two dimensions. Currently won't compile with vectorisation due to excessive inlining of dictionaries.

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 FB-DIMM; 256-bit-wide memory architecture; Mac OS X Server 10.5.6

Software spec: GHC 6.11 (from first week of Mar 09); gcc 4.0.1

Program	Problem size	sequential	P=1	P=2	P=4	P=8
SumSq, primitives	10M	22	40	20	10	5
SumSq, vectorised	10M	22	40	20	10	5
SumSq, ref C	10M	9	–	–	–	–
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	437	221	209
DotP, ref C	100M elements	–	458	235	210	210
SMVM, primitives	10kx10k @ density 0.1	119/119	111/111	78/78	36/36	21/21
SMVM, vectorised	10kx10k @ density 0.1	196/196	1220/1220	847/847	515/515	424/424
SMVM, ref C	10kx10k @ density 0.1	35	–	–	–	–
SMVM, primitives	100kx100k @ density 0.001	132/132	135/135	81/81	91/91	48/48
SMVM, vectorised	100kx100k @ density 0.001	214/214	1259/1259	899/899	556/556	429/429
SMVM, ref C	100kx100k @ density 0.001	46	–	–	–	–

All results are in milliseconds, and the triples report best/average/worst execution time (wall clock) of three runs. The column marked "sequential" reports times when linked against dph-seq and the columns marked "P=n" report times when linked against dph-par and run in parallel using the specified number of parallel OS threads.

Comments regarding SumSq

The versions compiled against dph-par are by factor of two slower than the ones linked against dph-seq.

However, found a number of general problems when working on this example:

• We need an extra -funfolding-use-threshold. We don't really want users having to worry about that.
• mapP (\x -> x * x) xs essentially turns into zipWithU (*) xs xs, which doesn't fuse with enumFromTo anymore. We have a rewrite rule in the library to fix that, but that's not general enough. We really would rather not vectorise the lambda abstraction at all.
• enumFromTo doesn't fuse due to excessive dictionaries in the unfolding of zipWithUP.
• Finally, to achieve the current result, we needed an analysis that avoids vectorising subcomputations that don't to be vectorised, and worse, that fusion has to turn back into their original form. In this case, the lambda abstraction \x -> x * x. This is currently implemented in a rather limited and ad-hoc way. We should implement this on the basis of a more general analysis.

Comments regarding DotP

Performance is memory bound, and hence, the benchmark stops scaling once the memory bus saturated. As a consequence, the wall-clock execution time of the Haskell programs and the C reference implementation are the same when all available parallelism is exploited. The parallel DPH library delivers the same single core performance as the sequential one in this benchmark.

Comments regarding smvm

There seems to be a fusion problem in DotP with dph-par (even if the version of zipWithSUP that uses splitSD/joinSD is used); hence the much lower runtime for "N=1" than for "sequential". The vectorised version runs out of memory; maybe because we didn't solve the bpermute problem, yet.

Execution on greyarea (1x UltraSPARC T2)

Hardware spec: 1x 1.4GHz UltraSPARC T2; 8 cores/processors with 8 hardware threads/core; 4MB on-die L2 cache per processor; FB-DIMM; Solaris 5.10

Software spec: GHC 6.11 (from first week of Mar 09) with gcc 4.1.2 for Haskell code; gccfss 4.0.4 (gcc front-end with Sun compiler backend) for C code (as it generates code that is more than twice as fast for numeric computations than vanilla gcc)

Program	Problem size	sequential	P=1	P=2	P=4	P=8	P=16	P=32	P=64
SumSq, primitives	10M	212/212	254/254	127/127	64/64	36/36	25/25	17/17	10/10
SumSq, vectorised	10M	212/212	254/254	128/128	64/64	32/32	25/25	17/17	10/10
SumSq, ref C	10M	120	–	–	–	–	–	–	–
DotP, primitives	100M elements	937/937	934/934	474/474	238/238	120/120	65/65	38/38	28/28
DotP, vectorised	100M elements	937/937	942/942	471/471	240/240	118/118	65/65	43/43	29/29
DotP, ref Haskell	100M elements	–	934	467	238	117	61	65	36
DotP, ref C	100M elements	–	554	277	142	72	37	22	20
SMVM, primitives	100kx100k @ density 0.001	1112/1112	1926/1926	1009/1009	797/797	463/ 463	326/326	189/189	207/207
SMVM, vectorised	100kx100k @ density 0.001	_|_	_|_	_|_	_|_	_|_	_|_	_|_	_|_
SMVM, ref C	100kx100k @ density 0.001	600	–	–	–	–	–	–	–

All results are in milliseconds, and the triples report best/worst execution time (wall clock) of three runs. The column marked "sequential" reports times when linked against dph-seq and the columns marked "P=n" report times when linked against dph-par and run in parallel using the specified number of parallel OS threads.

Comments regarding SumSq

The primitives scale nicely, but something is deeply wrong (lack of fusion, perhaps) with the vectorised version.

Comments regarding DotP

The benchmark scales nicely up to the maximum number of hardware threads. Memory latency is largely covered by excess parallelism. It is unclear why the Haskell reference implementation "ref Haskell" falls of at 32 and 64 threads. See also  a comparison graph between LimitingFactor and greyarea.

Comments regarding smvm

As on LimitingFactor, but it scales much more nicely and improves until using four threads per core. This suggets that memory bandwidth is again a critical factor in this benchmark (this fits well with earlier observations on other architectures). Despite fusion problem with dph-par, the parallel Haskell program, using all 8 cores, still ends up three times faster than the sequential C program.