Efficient GPU stream transform

Asymmetric data patterns and workloads pose a challenge to massively parallel algorithm design, in particular for modern wide- SIMD architectures exhibiting several levels of parallelism. We propose a simple-to use primitive that enables programmers to design algorithms with arbitrary data expansion or compaction while hiding the architecture details. We evaluate and characterize the performance of the primitive for a range of workloads, both synthetic and real-world. The results demonstrate that the primitive can be an effective tool in the toolbox of designers of parallel algorithms