Algorithm 980: Sparse QR Factorization on the GPU

作者：Yeralan Sencer Nuri^*; Davis Timothy A; Sid Lakhdar Wissam M; Ranka Sanjay

来源：ACM Transactions on Mathematical Software, 2017, 44(2): 17.

DOI：10.1145/3065870

• Algorithms
• Experimentation
• Performance
• QR factorization
• least-square problems
• sparse matrices
• GPU

Sparse matrix factorization involves a mix of regular and irregular computation, which is a particular challenge when trying to obtain high-performance on the highly parallel general-purpose computing cores available on graphics processing units (GPUs). We present a sparse multifrontal QR factorization method that meets this challenge and is significantly faster than a highly optimized method on a multicore CPU. Our method factorizes many frontal matrices in parallel and keeps all the data transmitted between frontal matrices on the GPU. A novel bucket scheduler algorithm extends the communication-avoiding QR factorization for dense matrices by exploiting more parallelism and by exploiting the staircase form present in the frontal matrices of a sparse multifrontal method.

• 出版日期2017-9
• 单位Microsoft