Pricing Python parallelism: a dynamic language cost model for heterogeneous platforms
Dejice Jacob, Phil W. Trinder, Jeremy Singer · 2020
Execution times may be reduced by offloading parallel loop nests to a GPU. Auto-parallelizing compilers are common for static languages, often using a cost model to determine when the GPU execution speed will outweigh the offload overheads. Nowadays scientific software is increasingly written in dynamic languages and would benefit from compute accelerators. The ALPyNA framework analyses moderately complex Python loop nests and automatically JIT compiles code for heterogeneous CPU and GPU architectures.