Using supplier locality in power-aware interconnects and caches in chip multiprocessors

Conventional snoopy-based chip multiprocessors take an aggressive approach broadcasting snoop requests to all nodes. In addition each node checks all received requests. This approach reduces the latency of cache to cache transfer misses at the expense of increasing power. In this paper we show that a large portion of interconnect/cache transactions are redundant as many snoop requests miss in the remote nodes.We exploit this inefficiency and introduce power optimization techniques for chip multiprocessors. Our optimizations rely on the observation that in a snoopy-based shared memory system the data supplier can be predicted with high accuracy. Our optimizations reduce power by eliminating unnecessary activity at both the requester and the supplier end of snoop requests.We reduce power as we (a) avoid broadcasting snoop requests to all processors and (b) avoid tag lookup for all nodes and for all requests arriving. In particular, we use supplier locality and introduce the following two optimizations.First, and at the requester end, we introduce speculative selective request (SSR) to reduce power dissipation in the binary tree interconnect. In SSR, we send the request only to the node more likely to have the missing data. We reduce power as we limit access only to the interconnect components between the requestor and the supplier node.Second, and at the supplier end, we propose speculative tag lookup (STL) to reduce power dissipation in data caches. We filter those accesses more likely to miss in the L1 cache.Using shared memory applications, we show that by limiting snoop requests to the speculated nodes we reduce interconnect power by 25% in a four-way multiprocessor. Moreover, we show that speculative tag lookup reduces power in tag arrays by 14.1% in a four-way multiprocessor. Both optimizations come with negligible performance loss and hardware overhead.