DataScalar Architectures

DataScalar architectures improve memory system performance by running computation redundantly across multiple processors, which are each tightly coupled with an associated memory. The program data set (and/or text) is distributed across these memories. In this execution model, each processor broadcasts operands it loads from its local memory to all other units. In this paper, we describe the benefits, costs, and problems associated with the DataScalar model. We also present simulation results of one possible implementation of a DataScalar system. In our simulated implementation, six unmodified SPEC95 binaries ran from 7% slower to 50% faster on two nodes, and from 9% to 100% faster on four nodes, than on a system with a comparable, more traditional memory system. Our intuition and results show that DataScalar architectures work best with codes for which traditional parallelization techniques fail. We conclude with a discussion of how DataScalar systems may accommodate traditional pa...

• Type: Conference
• Year: 1997
• Where: ISCA
• Authors: Doug Burger, Stefanos Kaxiras, James R. Goodman
• Keywords: Architectures Improve Memory, DataScalar Architectures, Hardware, ISCA 1997, Processor Broadcasts Operands