Kunle Olukotun is the Cadence Design Systems Professor of Electrical Engineering and Computer Science at Stanford University. Olukotun is relevant to NIM Optimization for systems and application analysis. First, the Stanford Pervasive Parallelism Lab  runs in the systems analysis style described in the book  Hennessy and Patterson, Computer Architecture: A Quantitative Approach.  The systems analysis formerly applied to  competing instruction set architectures, memory hierarchies, and network interconnects are now applied to parallel and distributed systems, GPUs, and even Domain Specific Languages for FPGA racks.  The fundamental underlying assumption is that the primary way Moore’s Law will continue to improve application performance year-over-year is through greater parallel system execution efficiency. From a NIM Optimization perspective if you are going to run a multibillion security portfolio through a Monte Carlo code to get the expected time series of cash flows you probably want to run in parallel so you can execute in a reasonable amount of time. An educated guess at this point is you want a high end cloud like Oracle or AWS, Xeon Phi, or GPU/ CUDA system to work from. FPGA compute servers stumbled several years ago on Wall Street and I would expect they will have difficulty getting traction with banks.

Second, the publications and talks from his lab sift through the various types of parallelism in real applications and prioritize which approaches and tools work best for which codes. Map/Reduce, graph algorithms,  transactional memory, and machine learning are the big money making applications. Scalability! But at what Cost? highlights some of the hazards of application analysis. NIM Optimization is an old school floating point intensive Monte Carlo followed by LP application. Seems like it is going to be an OpenMP and OpenMPI application on top of some highly vectorized code.