1. 1 Memory is the Network Krste Asanovic The Parallel Computing Laboratory EECS Department UC Berkeley NoCS Panel, May 13, 2009

2. One Slide Version of Talk  No-one can afford to build application-specific chips  at least not in feature sizes that warrant a NoC  All future chips are programmable and parallel  Multicore, Manycore, GPU, FPGA  Usable programmable parallel systems bottlenecked by memory system  Performance and energy-efficiency  Network is just a path to memory (on-chip and off- chip) - work on entire problem not just cabling  Change name to “International Symposium on Memory Systems”  (Large-scale networks between chips/boards/racks still very interesting to think of as networks, but that’s different.)

3. ApplicationChipApplication-Specific ProgrammableChip Application

4. Successful Parallel Programming Models   Producer-consumer easy   Mutual exclusion easy   No implicitly shared state   Sharing state cumbersome   Irregular computation hard   Examples: Occam, Simulink, StreamIt, Clik, … Actor Networks   Producer-consumer hard   Mutual exclusion hard   Transactional mem. helps   Sharing state easy   Maybe too easy   Examples: Pthreads, Cilk, Java, … Shared-Memory Dynamic Threads/Transactions fork join Data-Parallel/SPMD barrier   Producer-consumer easy   Handled en-masse   Mutual exclusion easy   Sharing state easy   Irregular computation hard   Examples: APL, NESL, Matlab, HPF, OpenMP, UPC, CAF, …

5. Memory is the Network-on-Chip from Software’s View Actors - messages buffered in memory-resident channels until convenient to run actor Data-Parallel - memory holds arrays used to interchange data between parallel phases Transactional - memory holds shared data base accessed atomically Programming with data on-flight on wires is too brittle for any large code (sorry Anant), need flexibility in when and where code gets executed

6. Fixed-function accelerators  Any programmable chip will have a stack of fixed-function accelerators  Crypto, Codecs, Radios, Graphics  But these won’t use NoC internally, just place and route  They’ll connect to general-purpose portion through memory for all reasons given before

7. Research Directions  Make memory a better communication channel  Richer software interface  Better synchronization primitives E.g., atomic message enqueue/dequeue for actor channels Atomic fetch-and-op for data-parallel apps Transactional memory for concurrent apps  Better cache-coherence protocols  Make memory go faster and with lower power  New device technologies (e.g., photonics)  New microarchitectures and network ideas  Must consider on-chip and off-chip to main memory at same time