1. 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

2. 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!

3. Target Hot Data Highly repetitious memory sequences
Hot sequences are objects long
Hot data
90% of program references
80% of cache misses

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

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

6. 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

7. 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

8. 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?