PARALUTION - a Library for Iterative Sparse Methods on CPU and GPU

Dimitar Lukarski
Division of Scientific Computing
Department of Information Technology
Uppsala University


The modern CPU and GPU technologies provide great potential for fast scientific computing. Only by exploring a fine-grained level of parallelism we can obtain the peak performance of these systems. Typically, this is a hardware specific task and requires different programming models/languages for each individual platform. This leads to hardware specific code and problem isolated solutions. In particular this affects the design of the software for sparse matrix computation.

PARALUTION is a C++ library for iterative sparse methods, where the main focus is the utilization of multi/many-core platforms and accelerators. In this talk, we describe the main design decisions and the chosen abstraction model of the library. Based on that, we are able to build portable preconditioned iterative methods which can be executed on GPU and CPU systems without any code modifications.

Furthermore, we detail the fine-grained parallel preconditioners for multi/many-core devices - their design and implementation. This covers ILU (power(q)-pattern method), multi-elimination (I)LU, additive and approximate inverse schemes.

We conclude with a few code snap shots and performance results. Last but not least, we will tell you about the first users of PARALUTION, our road-map and collaborators. Here is the web page for the library.