Search in mobile applications has traditionally been keyword driven and limited to simple queries, such as searching for product names, even when the apps support much richer, transactional experiences. On the other hand, search on the web has evolved into queries that are complex, objective-based and most often in natural language. The recent advances in Generative AI make it possible to bring the power of web-like, conversational searches into mobile applications. In this talk, we present the various problems, opportunities and challenges in harnessing LLMs to augment the traditional search experience in mobile applications.

Graphics Processing Units (GPUs) are massively parallel, many-core processors with tremendous computational power and very high memory bandwidth. With the advent of general purpose programming models such as NVIDIA's CUDA and the new standard OpenCL, general purpose programming using GPUs (GPGPU) has become very popular. However, the GPU architecture and programming model have brought along with it many new challenges and opportunities for compiler optimizations. One such classical optimization is loop unrolling. Current GPU compilers perform limited loop unrolling. In this paper, we attempt to understand the impact of loop unrolling on GPGPU programs. We develop a semi-automatic, compile-time approach for identifying optimal unroll factors for suitable loops in GPGPU programs. In addition, we propose techniques for reducing the number of unroll factors evaluated, based on the characteristics of the program being compiled and the device being compiled to. We use these techniques to evaluate the effect of loop unrolling on a range of GPGPU programs and show that we correctly identify the optimal unroll factors. The optimized versions run up to 70 percent faster than the unoptimized versions.