Blaze is an interesting effort with relatively easy programming interface (see for example CG code: http://code.google.com/
p/blaze-lib/wiki/Getting_ Started under "A complex example").
The main guy behind the software is Klaus Iglberger, a PhD from germany. Two papers were published about Blaze:
- K. Iglberger, G. Hager, J. Treibig, and U. Rüde: Expression Templates Revisited: A Performance Analysis of Current Methodologies(Download). SIAM Journal on Scientific Computing, 34(2): C42--C69, 2012
- K. Iglberger, G. Hager, J. Treibig, and U. Rüde: High Performance Smart Expression Template Math Libraries (Download). Proceedings of the 2nd International Workshop on New Algorithms and Programming Models for the Manycore Era (APMM 2012) at HPCS 2012
Regarding performance, at least for the tested primitives they have very good performance. It seems they work very well for small matrices (less than 100 width) even relative to MKL. For larger matrices have performance similar to MKL. The performance tests do not cover sparse matrices (only dense).
What is missing IMHO, is an algorithmic suite like linear solvers, svd etc. that exists Eigen. So currently Blaze focuses mainly on matrix vector operation.
It seems Blaze is using a single core implementation (they do not exploit parallelism).
Overall, it seems like a very interesting project to keep track of. Once it supports some additional functionality I would consider using it.
To dig a little dipper, I sent some questions to Klaus Iglberger, who was very kind to promptly replay:
> We were looking for a good math library to replace Eigen and we liked Blaze API. But we still have some missing functionality I wanted to ask you about.We released the Blaze library only recently, in August 2012. Therefore Blaze is obviously much younger than Eigen and cannot compete in terms of features. Currently it can only compete in terms of performance (it seems to be the most efficient C++ math library for many operations) and in terms of software architecture and design. The software design and architecture is one of our personal interests will therefore always play a major role in the development of Blaze. However, due to that effort, I feel that Blaze can be used more naturally than the other C++ math libraries (including Eigen).
Since you are asking about features, let me give you an idea of our current roadmap. We are currently working on views (which you can for instance use to work on submatrices, extract parts of the result from vectors and matrices, etc.), special purpose matrices (banded matrices, upper and lower triangle matrices, etc.) and shared memory parallelization. These will be the next big features, starting with views in Blaze 1.2.
Whereas in direct comparison Blaze cannot compete in the total number of features, Blaze still offers a small number of unique features. The probably most important is the support of the Intel MIC architecture (Xeon Phi). Second is the support of the AVX instruction set, that is still not available in most other C++ math libraries. Third, Blaze is probably the only library that allows a completely hierarchic nesting of matrix and vector data types without performance penalties. For instance, you can define block structured matrices very conveniently:
typedef CompressedMatrix< DynamicMatrix<double,rowMajor>
BlockStructuredMatrix A, B, C;
// … Initializing the matrices
C = A * B;
In this matrix multiplication you can still count on every single matrix multiplication to be executed at maximum performance (see also the answer to your third question). And last but not least, with the introduction of views, Blaze will offer an extremely versatile feature to restrict the computation to the "parts" you are interested in:
DynamicMatrix<double,rowMajor> A, B;
// Restricts the computation of the matrix multiplication to the fourth column and still considers the most efficient way
// to compute the result although both matrices are stored in a row-wise fashion.
x = column( A * B, 4 );
> 1) Is there a plan to support sparse matrix algorithms like solving a linear system, eigen decomposition etc.
> 2) What is the level of support for parallelism? Namely, is the library fully serial or do you have some support for parallelism when there are multiple cores.
> 3) According to the performance plots, on large matrices blaze performance aligns with MKL. Is there some mechanism which sends the computation to MKL once the problem is big enough and otherwise uses blaze code?
> 4) Is there support for serialization for writing and loading matrices from file?
> Thanks a lot for your time!