John-Paul Fryckman CSE 231: Paper Presentation 23 May 2002 Dynamic Hot Data Stream Prefetching for General-Purpose Programs Chilimbi and Hirzel John-Paul Fryckman CSE 231: Paper Presentation 23 May 2002

Why Prefetching? Increasing memory latencies Not enough single thread ILP to hide memory latencies Minimizing stall cycles due to misses increases IPC (increases performance) Fetch data before it is needed!

Target Hot Data Highly repetitious memory sequences Hot sequences are 15-20 objects long Hot data 90% of program references 80% of cache misses

Why Dynamic Dynamic prefetching translates into a general purpose solution Many unknowns at compile time Pointer chasing code Irregular strides

Dynamic Hot Data Stream Prefetching Profile memory references Detect hot data streams Create and insert triggers for these streams And, repeat!

Profiling and Detection Need to minimize profiling overhead Use sampling Switch into instrumented code Collect traces Find hot data streams Generate context-free grammars for hot sequences

DFSM Prefetching Engine Merge CFGs together into a massive DFSM DFSM detects prefixes for hot sequences, then generates fetches for the rest of the data Insert prefetching code Presumably, states are removed when they are no longer hot

Good and the Not So Good Good: Questionable Questions: With overhead, 5-19% speedups Questionable Questions: How does it impact easy to predict code? Worse case state for DFSM: O(2n) They did not study this. Is this possible? Do they always prefetch in time? What about phase changes/cache pollution?