ABSTRACTCarnegie Mellon, School of Computer ScienceMemory Coherence Activity Prediction in Commercial Workloads Stephen Somogyi, Thomas F. Wenisch, Nikolaos Hardavellas, Jangwoo Kim, Anastassia Ailamaki, Babak Falsafi Carnegie Mellon University
Recent research indicates that prediction-based coherence optimi-zations offer
substantial performance improvements for scientific applica-tions in distributed
shared memory multiprocessors. Important commercial applications also show
sensitivity to coherence latency, which will become more acute in the future as
technology scales. Therefore it is important to in-vestigate prediction of memory
coherence activity in the context of commer-cial workloads. FULL PAPER: pdf |