Version 50 (modified by benl, 7 years ago) (diff)

Update numbers

Status of DPH Benchmarks

This page gives an overview of how well the benchmarks in the dph-examples/ directory of package dph are currently working.

The benchmarks are run each night by DPH BuildBot. The results are posted to cvs-ghc and uploaded to Check there for the latest numbers.




  • Either dph or repa. Repa programs use the same parallel array library as DPH, but do not go through the vectorising transform.


  • vectorised means it's been through the DPH vectorising transform.
  • vector is a hand written version using immutable Data.Vectors
  • vector-mutable is a hand written version using mutable Data.Vectors.
  • vector-immutable means the same as vector and is used when there is also an mutable version.


  • Whether a benchmark is natively parallel or sequential.
  • Parallel versions are also run single threaded (with -N1) and sequential versions are also run with (-N4) so we get the parallel GC.
  • Parallel versions with -N1 will tend to be slower than natively sequential versions due to overheads for supporting parallelism.

Statically Nested

Statically nested parallelism is where the parallelism has a fixed, finite depth. For example mapP f (filterP g xs). Statically nested programs are easier to vectorize than dynamically nested programs. At present, single threaded statically nested programs should run as fast as equivalent Data.Vector programs. Parallel versions should display a good speedup.

Computes the sum of the squares from 1 to N using Int. N = 100M.
name runtime speedup notes
dph.sumsq.vector.seq.N4 404ms 1
dph.sumsq.vectorised.seq.N4 434ms 0.93
dph.sumsq.vectorised.par.N1 443ms 0.91
dph.sumsq.vectorised.par.N2 222ms 1.82
dph.sumsq.vectorised.par.N4 111ms 3.63

Summary: fine
Todo: Add the sequential C version.

Computes the dot product of two vectors of Doubles. N=10M.
name runtime speedup notes
dph.sumsq.vector.seq.N4 68ms 1
dph.sumsq.vectorised.seq.N4 58ms 1.17 A
dph.sumsq.vectorised.par.N1 55ms 1.24
dph.sumsq.vectorised.par.N2 33ms 2.06
dph.sumsq.vectorised.par.N4 25ms 2.72

A: The vectorised version is faster than with Data.Vector. Why was this?

Summary: fine.
Todo: Add the sequential C version.

Multiplies a dense vector with a sparse matrix represented in the compressed sparse row format (CSR).

Todo: Add this to the nightly run.

Dynamically Nested

Dynamically nested programs have a recursive structure where each level of the recursion invokes more parallel computations. This is common for benchmarks that use divide-and-conquer style algorithms.

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).
Given a set of points (in a plane), compute the sequence of points that encloses all points in the set. This benchmark is interesting as it is the simplest code that exploits the ability to implement divide-and-conquer algorithms with nested data parallelism. We have only a "vectorised" version of this benchmark and a sequential Haskell reference implementation, "ref Haskell", using vanilla lists.

Dynamically Nested with Algebraic Data Types

These programs also use user defined algebraic data types. Vectorization of these programs is still a work in progress.

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