Corrosion Fatigue Behaviour of Laser-welded Shape Memory NiTi Wires in a Simulated Body Fluid

Corrosion fatigue is a fracture process as a consequence of synergistic interactions between the material structure, corrosive environment and cyclic loads/strains. It is difficult to be detected and can cause unexpected failure of engineering components in use. This study reveals a comparison of corrosion fatigue behaviour of laser-welded and bare NiTi wires using bending rotation fatigue (BRF) test coupled with a specifically-designed corrosion cell. The testing medium was Hanks’ solution (simulated body fluid) at 37.5 oC. Electrochemical impedance spectroscopic (EIS) measurement was carried out to monitor the change of corrosion resistance of sample during the BRF test at different periods of time. Experiments indicate that the laser-welded NiTi wire would be more susceptible to the corrosion fatigue attack than the bare NiTi wire. This study can serve as a benchmark for the product designers and engineers to understand the corrosion fatigue behaviour of the NiTi laser weld joint and determine the fatigue life safety factor for NiTi medical devices/implants involving laser welding in the fabrication process.

Energy Optimization of Memory Intensive Parallel Workloads

Energy consumption is an important concern in modern multicore processors. The energy consumed by a multicore processor during the execution of an application can be minimized by tuning the hardware state utilizing knobs such as frequency, voltage etc. The existing theoretical work on energy minimization using Global DVFS (Dynamic Voltage and Frequency Scaling), despite being thorough, ignores the time and the energy consumed by the CPU on memory accesses and the dynamic energy consumed by the idle cores. This article presents an analytical energy-performance model for parallel workloads that accounts for the time and the energy consumed by the CPU chip on memory accesses in addition to the time and energy consumed by the CPU on CPU instructions. In addition, the model we present also accounts for the dynamic energy consumed by the idle cores. The existing work on global DVFS for parallel workloads shows that using a single frequency for the entire duration of a parallel application is not energy optimal and that varying the frequency according to the changes in the parallelism of the workload can save energy. We present an analytical framework around our energy-performance model to predict the operating frequencies (that depend upon the amount of parallelism) for global DVFS that minimize the overall CPU energy consumption. We show how the optimal frequencies in our model differ from the optimal frequencies in a model that does not account for memory accesses. We further show how the memory intensity of an application affects the optimal frequencies.

Managed Acceleration for In-Memory Database Analytic Workloads

In-Memory Databases (IMDBs), such as SAP HANA, enable new levels of database performance by removing the disk bottleneck and by compressing data in memory. The consequence of this improved performance means that reports and analytic queries can now be processed on demand. Therefore, the goal is now to provide near real-time responses to compute and data intensive analytic queries. To facilitate this, much work has investigated the use of acceleration technologies within the database context. While current research into the application of these technologies has yielded positive results, they have tended to focus on single database tasks or on isolated single user requests. This paper uses SHEPARD, a framework for managing accelerated tasks across shared heterogeneous resources, to introduce acceleration into an IMDB. Results show how, using SHEPARD, multiple simultaneous user queries all receive speed-up by using a shared pool of accelerators. Results also show that offloading analytic tasks onto accelerators can have indirect benefits for other database workloads by reducing contention for CPU resources.