Algorithm XXX: VTDIRECT95: Serial and Parallel Codes for the Global Optimization Algorithm DIRECT

VTDIRECT95 is a Fortran 95 implementation of D.R. Jones' deterministic global optimization algorithm called DIRECT, which is widely used in multidisciplinary engineering design, biological science, and physical science applications. The package includes both a serial code and a data-distributed massively parallel code for different problem scales and optimization (exploration vs. exploitation) goals. Dynamic data structures are used to organize local data, handle unpredictable memory requirements, reduce the memory usage, and share the data across multiple processors. The parallel code employs a multilevel functional and data parallelism to boost concurrency and mitigate the data dependency, thus improving the load balancing and scalability. In addition, checkpointing features are integrated into both versions to provide fault tolerance and hot restarts. Important alogrithm modifications and design considerations are discussed regarding data structures, parallel schemes, error handling, and portability. Using several benchmark functions and real-world applications, the software is evaluated on different systems in terms of optimization effectiveness, data structure efficency, parallel performance, and checkpointing overhead. The package organization and usage are also described in detail

gpusvcalibration: A R Package for Fast Stochastic Volatility Model Calibration Using GPUs

In this paper we describe the gpusvcalibration R package for accelerating stochastic volatility model calibration on GPUs. The package is designed for use with existing CRAN packages for optimization such as DEOptim and nloptr. Stochastic volatility models are used extensively across the capital markets for pricing and risk management of exchange traded financial options. However, there are many challenges to calibration, including comparative assessment of the robustness of different models and optimization routines. For example, we observe that when fitted to sub-minute level midmarket quotes, models require frequent calibration every few minutes and the quality of the fit is routine sensitive. The R statistical software environment is popular with quantitative analysts in the financial industry partly because it facilitates application design space exploration. However, a typical R based implementation of a stochastic volatility model calibration on a CPU does not meet the performance requirements for sub-minute level trading, i.e. mid to high frequency trading.We identified the most computationally intensive part of the calibration process in R and off-loaded that to the GPU.We created a map-reduce interface to the computationally intensive kernel so that it can be easily integrated in a variety of R based calibration codes using our package. We demonstrate that the new R based implementation using our package is comparable in performance to aC=C++ GPU based calibration code

The Collaborative Development of New CFD Methods Adapted for Tilt Rotor Aircraft in the HiPerTilt Project

No abstract available

Fast Recompilation of Object Oriented Modules

Once a program file is modified, the recompilation time should be minimized, without sacrificing execution speed or high level object oriented features. The recompilation time is often a problem for the large graphical interactive distributed applications tackled by modern OO languages. A compilation server and fast code generator were developed and integrated with the SRC Modula-3 compiler and Linux ELF dynamic linker. The resulting compilation and recompilation speedups are impressive. The impact of different language features, processor speed, and application size are discussed