The 2010 MEMOCODE Hardware Software Co-design challenge is to implement a Deep Packet Inspection architecture, called the CANSCID - Combined Architecture for Stream Categorization and Intrusion Detection. In this short paper, we present the design details of our submission, that utilizes a Graphical Processing Unit (GPU) to accelerate the parallel regular expression matching. The target line rate of 500 Mbps is met on all of the 25 mandatory and 10 optional patterns. The design is developed using the NVIDIA CUDA framework and tested on the Tesla GPU.

Wide Single Instruction, Multiple Thread (SIMT) architectures often require a static allocation of thread groups that are executed in lockstep throughout the entire application kernel. Individual thread branching is supported by executing all control flow paths for threads in a thread group and only committing the results of threads on the current control path. While convergence algorithms are used to maximize processor efficiency during branching operations, applications requiring complex control flow often result in low processor efficiency due to the length and quantity of control paths. Global rendering algorithms are an example of a class of application that can be accelerated using a large number of independent parallel threads that each require complex control flow, resulting in comparatively low efficiency on SIMT processors. To improve processor utilization for global rendering algorithms, we introduce a SIMT architecture that allows for threads to be created dynamically at runtime. Large application kernels are broken down into smaller code blocks we call μ-kernels that dynamically created threads can execute. These runtime μkernels allow for the removal of branching statements that would cause divergence within a thread group, and result in new threads being created and grouped with threads beginning execution of the same μ-kernel. In our evaluation of SIMT processor efficiency for a global rendering algorithms, dynamic μ-kernels improved processor performance by an average of 1.4x.

Streamline integration of fields produced by computational fluid mechanics simulations is a commonly used tool for the investigation and analysis of fluid flow phenomena. Integration is often accomplished through the application of ordinary differential equation (ODE) integrators--integrators whose error characteristics are predicated on the smoothness of the field through which the streamline is being integrated--smoothness which is not available at the inter-element level of finite volume and finite element data. Adaptive error control techniques are often used to ameliorate the challenge posed by inter-element discontinuities. As the root of the difficulties is the discontinuous nature of the data, we present a complementary approach of applying smoothness-enhancing accuracy-conserving filters to the data prior to streamline integration. We investigate whether such an approach applied to uniform quadrilateral discontinuous Galerkin (high-order finite volume) data can be used to augment current adaptive error control approaches. We discuss and demonstrate through numerical example the computational trade-offs exhibited when one applies such a strategy.