Hybrid computing: CPU+GPU co-processing and its application to tomographic reconstruction.

Modern computers are equipped with powerful computing engines like multicore processors and GPUs. The 3DEM community has rapidly adapted to this scenario and many software packages now make use of high performance computing techniques to exploit these devices. However, the implementations thus far are purely focused on either GPUs or CPUs. This work presents a hybrid approach that collaboratively combines the GPUs and CPUs available in a computer and applies it to the problem of tomographic reconstruction. Proper orchestration of workload in such a heterogeneous system is an issue. Here we use an on-demand strategy whereby the computing devices request a new piece of work to do when idle. Our hybrid approach thus takes advantage of the whole computing power available in modern computers and further reduces the processing time. This CPU+GPU co-processing can be readily extended to other image processing tasks in 3DEM.

Fast tomographic reconstruction on multicore computers.

SUMMARY: Tomo3D implements a multithreaded vectorized approach to tomographic reconstruction that takes full advantage of the computer power in modern multicore computers. Full resolution tomograms are generated at high speed on standard computers with no special system requirements. Tomo3D has the most common reconstruction methods implemented, namely weighted Back-projection (WBP) and simultaneous iterative reconstruction technique (SIRT). It proves to be competitive with current graphic processor unit solutions in terms of processing time, in the order of a few seconds with WBP or minutes with SIRT. The program is compatible with standard packages, which easily allows integration in the electron tomography workflow.

Vectorization with SIMD extensions speeds up reconstruction in electron tomography.

Electron tomography allows structural studies of cellular structures at molecular detail. Large 3D reconstructions are needed to meet the resolution requirements. The processing time to compute these large volumes may be considerable and so, high performance computing techniques have been used traditionally. This work presents a vector approach to tomographic reconstruction that relies on the exploitation of the SIMD extensions available in modern processors in combination to other single processor optimization techniques. This approach succeeds in producing full resolution tomograms with an important reduction in processing time, as evaluated with the most common reconstruction algorithms, namely WBP and SIRT. The main advantage stems from the fact that this approach is to be run on standard computers without the need of specialized hardware, which facilitates the development, use and management of programs. Future trends in processor design open excellent opportunities for vector processing with processor's SIMD extensions in the field of 3D electron microscopy.