1. CAPS project-team Compilation et Architectures pour Processeurs Superscalaires et Spécialisés

2. André Seznec CAPS project-team Irisa-Inria 2 History of CAPS project-team  Project-team created in 1994:  “Compiler Parallel Architectures and Systems”  Common focus: high performance through optimizing the memory hierarchy  Comes from supercomputer architecture group:  Involved in Marie mini-supercomputer design  late participation in ACRI

3. André Seznec CAPS project-team Irisa-Inria 3 CAPS: Compiler and Architecture for Superscalar and Special purpose processors  Two interacting activities  microprocessor architecture (A. Seznec, P. Michaud) High performance Migrating high performance concepts to embedded systems  Performance oriented compilation (F. Bodin) High performance Embedded processors + Recently: Worst case execution time analysis (I. Puaut)

4. André Seznec CAPS project-team Irisa-Inria 4 CAPS « missions »  Defining the tradeoffs between:  what should be done through hardware  what can be done by the compiler  for maximum performance  or for minimum cost  or for minimum size, power..

5. André Seznec CAPS project-team Irisa-Inria 5 Issues on high performance processor architecture  Memory hierarchy management:  1 cycle L1 – 10 cycles L2 – 30 cycles L3 – 200 cycles memory  Branch prediction :  30 cycles penalty x N instructions per cycle  Single cycle next instruction block address generation ?  Complexity quadratic with issue width:  Register file, bypass network, issue logic  Single chip hardware thread parallelism is available:  How do we exploit it ?  Power/temperature

6. André Seznec CAPS project-team Irisa-Inria 6 Issues on code generation/software environments for embedded processors  ILP, caches are entering embedded processor world  Code generation must manage them  Binary compatibility is not critical, time-to-market is critical  Retargetable platforms are wanted: ISAs, architecture  Performance is not the only ultimate goal:  Code size/ performance  Power/ performance  System cost/ performance

7. André Seznec CAPS project-team Irisa-Inria 7 Recent scientific contributions (1) Processor architecture  Global history branch predictors and instruction fetch front-end  2bcgskew used in Compaq EV8  Pipelining the I-fetch front end  Limiting hardware complexity on superscalar processors  Dataflow prescheduling: instruction window  WSRS architecture: register file, bypass network and issue logic  Thread parallelism and single chip parallelism :  CASH: CMP and SMT hybrid  Execution migration: single thread on a multicore, to use all the cache space

8. André Seznec CAPS project-team Irisa-Inria 8 Recent scientific contributions (2) architecture/compiler interaction  ISA simulation:  ABSCISS : ISA and architecture retargetable high speed simulator for VLIW processor  IATO : simulation of out-of-order execution IA64  Low power and architecture configurability:  Cache reconfiguration at software level on phase basis  Hardware/software speculative management of data path and register file width  SWARP: retargetable C-to-C preprocessor to enhance multimedia instruction use

9. André Seznec CAPS project-team Irisa-Inria 9 Recent scientific contributions (3) compiler and software environments  Artificial intelligence in performance tuning  CAHT: case based reasoning for assisting performance tuning  Automatic derivation of compiler heuristics: using machine learning to derive compiler heuristics  Performance code size tradeoffs:  Iterative compilation  Mixing interpretation on compressed code and native execution

10. André Seznec CAPS project-team Irisa-Inria 10 “New-CAPS” objectives (1)  High-end microprocessor architecture: From “ultimate performance” to “ maintaining performance to cheaper”  Migrating “high-end” concepts to embedded processors:  (limited) O-O-O execution  Compiler/architecture power management

11. André Seznec CAPS project-team Irisa-Inria 11 “New-CAPS” research objectives (2)  Embedded systems are more and more complex:  performance often comes with unpredictability and unstabibility Dimensioning a system ? Real time constraints ?  Research on performance predictability and stability:  Predictable/stable performance oriented code generation  Predictable/stable performance oriented architecture

12. André Seznec CAPS project-team Irisa-Inria 12 “New-CAPS” research objectives (3)  On-chip thread parallelism is a new opportunity:  Homogeneous: SMT/CMP Tradeoffs, sharing, synchronization  Heterogeneous: single ISA –Power, performance, multiple ISAs (e.g. SoC) –Thread extraction

13. What can we bring in SCIPARC at architecture level ?

14. André Seznec CAPS project-team Irisa-Inria 14 CAPS pipeline background  « ancient » background in hardware management of ILP: both research and implementation  decoupled pipeline architectures: Involved in the design of Marie mini-supercomputer 86- 88  OPAC, an hardware matrix floating-point coprocessor 1991: 300 ICs, a VLSI sequencer,..

15. André Seznec CAPS project-team Irisa-Inria CAPS background in microarchitecture  Solid knowledge in microprocessor architecture  technological watch on microprocessors  + research on processor architecture  + A. Seznec worked at Alpha Development Group in 1999- 2000: Defined the EV8 branch predictor  + P. Michaud worked at Intel (2001-2002)

16. André Seznec CAPS project-team Irisa-Inria 16 Background in memory hierarchy  Interleaved memories for vector supercomputers (research):  + A. Seznec participated at Tarantula project: vector extension to Compaq EV8  International CAPS visibility in cache architecture :  skewed associative cache  + decoupled sectored cache

17. André Seznec CAPS project-team Irisa-Inria 17 Our expertise may help to define next machine in SCIPARC  Bring pipeline definition expertise  Bring memory hierarchy definition expertise  Help to remain simple  Help to enlarge possible application domains