Dynamic Load Scheduling on CPU-GPU for Iterative Tomographic Reconstruction

This work presents a hybrid computing approach which combines GPUs and multicore processors to fully take advantage of the computing power latent in modern computers. It also presents its application to the problem of tomographic reconstruction. One inherent characteristic of these modern platforms is their heterogeneity, which raises the issue of workload distribution among the different processing elements. Adaptive load balancing techniques are thus necessary to properly adjust the amount of work to be done by each computing element. Here, we have chosen the ´on-demand´ strategy, a well-known technique in the HPC field by which the different elements asynchronously request a piece of work when they become idle, thereby keeping the system fairly well balanced. The results show that our scheme accommodates to the heterogeneous platform where it runs as it assigns more work to the faster processing elements automatically, which allows to correctly exploit all the resources available and to get complete reconstructions in less time than pure CPU or GPU approaches.