Tuesday, August 29, 2006

FPGA Computing and Virtualization

Today was my last day at Xilinx. I fly away from the west coast tomorrow morning and head back to MIT to begin my year as a grad student. Hopefully I will emerge unscathed.

I've been thinking a lot about the implications of software virtualization to FPGA computing. I'm intrigued by the idea of implementing a hypervisor in a tightly coupled FPGA/CPU system. I imagine a system with an FPGA connected to multiple memory units and managing the data flow for the various active virtual machines. The software layer of the hypervisor instructs the FPGA to move data to the processor or to function accelerators within the FPGA. The FPGA would probably host the hypervisor agent in order to concurrently manage instructions from the various virtual machines. Instructions non-native to the CPU may either be executed within the FPGA or "decoded" in the FPGA and passed along to the CPU.

If a virtualization layer sits just above the OS layer consisting of a resource sharing scheduler and a dynamic load balancer then applications would be able to take advantage of fine-grained optimizations while running on a virtualized compatibility layer. The key to making a massively scalable operating system is providing mechanisms for high level agents to inherit from low level optimization. A method for agents to manage "costs" and resource sharing provides an elegant optimization strategy that spans across all granularity levels.

1 comment:

raja neogi said...

There are VM frameworks that leverage multi-core, multi-threaded machines and FPGA (reference: ICPADS-2006).

The framework includes a functional language (scripting language) and an interpreter. Dense computations (policies) are dispatched to virtual accelerators (mapped to FPGA) for performance.

Marrying coarse grained parallel machines with fine grained parallel machines to extract performance for demanding applications, is still an emerging field. The challenge really is to "load balance in VM frameworks" for optimal performance without loosing sight of productivity.