EM Photonics and a few members of the CULA team will be attending SPIE's Defense, Security, & Sensing (DSS) conference next week in Orlando, Florida. In addition to a booth in the exhibit hall, we'll be presenting a number of papers including one detailing the latest work involving our sparse linear algebra solvers. If you are attending the conference, please stop by our booth or visit one of our talks!
Here is the abstract for our sparse linear algebra talk:
The modern graphics processing unit (GPU) found in many standard personal computers is a highly parallel math processor capable of over 1 TFLOPS of peak computational throughput at a cost similar to a high-end CPU with excellent FLOPS-to-watt ratio. High-level sparse linear algebra operations are computationally intense, often requiring large amounts of parallel operations and would seem a natural fit for the processing power of the GPU. Our work is on a GPU accelerated implementation of sparse linear algebra routines. We present results from both direct and iterative sparse system solvers.
The GPU execution model featured by NVIDIA GPUs based on CUDA demands very strong parallelism, requiring between hundreds and thousands of simultaneous operations to achieve high performance. Some constructs from linear algebra map extremely well to the GPU and others map poorly. CPUs, on the other hand, do well at smaller order parallelism and perform acceptably during low-parallelism code segments. Our work addresses this via hybrid a processing model, in which the CPU and GPU work simultaneously to produce results. In many cases, this is accomplished by allowing each platform to do the work it performs most naturally. For example, the CPU is responsible for graph theory portion of the direct solvers while the GPU simultaneously performs the low level linear algebra routines.
We'll also be presenting and demonstrating work from our image processing and fluid dynamics teams.