Yale MOLAR is an in-house Positron Emission Tomography (PET) image reconstruction application written in C++ and MPI. It deals with hundreds of millions of lines-of-response (LORs) independently to reconstruct an image. The nature of the image reconstruction process makes MOLAR an ideal candidate for GPU acceleration. In this study, we present our work on accelerating MOLAR with CUDA, and show the results that demonstrate the effectiveness and correctness of our CUDA implementation. Overall, Yale MOLAR with CUDA runs up to 6 times faster than the CPU-only code, reducing a typical high resolution image reconstruction time from several hours to less than one hour.

Tomography based imaging is used in various disciplines with the most widely known as computed tomography (CT). In general, they have in common that a signal is measured on the specimen's surface and at multiple locations to reconstruct some type of a material property distribution using application specific constitutive equations. In this paper and for the first time with experimental validation, a novel tomography approach is successfully introduced based on the mechanics of solids. It utilizes the equations of equilibrium at its core embedded in an optimization framework to control the ill-posed nature of these problems. Measured signals are deformation fields obtained from a digital image correlation system with photo cameras. This work demonstrates the potential of mechanics based tomography (MBT) on a composite silicone material. This work encourages to investigate MBT as a breast tumor screening tool to visualize tumors based on their stiffness contrast. Another long-term goal could be to investigate the use of this methodology as a non-destructive testing tool for civil engineering structures such as bridges. Finally, this new methodology could potentially be miniaturized with stereoscopes and extended to map the material property distribution at the microstructure, e.g. grain length-scale.

Open OnDemand (OOD) greatly lowers the barrier to entry to high performance computing (HPC) resources and facilitates usage for new and experienced users. Moreover, using OOD for courses with computational components enables lecturers and teaching assistants to focus on the course instead of on-boarding students. To these ends the Yale Center for Research Computing (YCRC) adopted OOD to make its advanced cyberinfrastructure more accessible to researchers and students across the university. While the use of single sign-on authentication in OOD makes HPC clusters easier to access, as implemented this authentication method limits users to a single HPC account per university-provided identity. The YCRC provides separate and temporary course-specific accounts to isolate course usage, which presented a challenge for supporting users with pre-existing research allocations or who are participating in multiple courses. In this paper, we present an easy to implement and maintain solution that separates traffic for each course and maps a single user identity to multiple HPC accounts. This solution can be used for other situations other than courses; wherever one single sign-on identity should access multiple HPC accounts.

We present for the first time the feasibility to recover the stiffness (here shear modulus) distribution of a three-dimensional heterogeneous sample using measured surface displacements and inverse algorithms without making any assumptions about local homogeneities and the stiffness distribution. We simulate experiments to create measured displacements and augment them with noise, significantly higher than anticipated measurement noise. We also test two-dimensional problems in plane strain with multiple stiff inclusions. Our inverse strategy recovers the shear modulus values in the inclusions and background well, and reveals the shape of the inclusion clearly.