1. Fast Paths in Concurrent Programs Wen Xu, Princeton University Sanjeev Kumar, Intel Labs. Kai Li, Princeton University

2. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs2 Processor 2 Processor 1 Concurrent Programs  Message-Passing Style  Processes & Channels  E.g. Streaming Languages C1C3 C2 P2P3 P4 P1  Uniprocessors  Programming Convenience ─ Embedded devices ─ Network Software Stack ─ Media Processing  Multiprocessors  Exploit parallelism  Partition Processes Problem: Compile a concurrent program to run efficiently on a Uniprocessor

3. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs3 Compiling Concurrent Programs  Process-based Approach  Keep processes separate  Context Switch between the processes  Small executable  Sum of Processes  Significant overhead  Automata-based Approach  Treat each process as a state machine  Combine the state machines  Small Overhead  Large Executables  Potentially Exponential  One Study Compared the two approaches and found:  Compared to Process-based approach, the Automata-based Approach generates code that is ─ Twice as fast ─ 2-3 Orders of magnitude larger executable  Neither approach is satisfactory

4. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs4 Our Work  Our Goal: Compile Concurrent Programs  Automated using a Compiler  Low Overhead  Small Executable Size  Our Approach: Combine the two approaches  Use process-based approach to handle all cases  Use automata-based approach to speed up the common cases

5. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs5 Outline  Motivation  Fast Paths  Fast Paths in Concurrent Programs  Experimental Evaluation  Conclusions

6. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs6 Fast Paths  Path: A dynamic execution path in the program  Fast Path or Hot Path: Well-known technique  Commonly-executed Paths (Hot Path)  Specialize and Optimize (Fast Path)  Two components  Predicate that specifies the fast path  Optimized code to execute the fast path  Compilers can be used to automate it  Mostly in sequential Programs

7. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs7 Manually implementing Fast Paths  To achieve good performance in Concurrent programs  Start: Insert code that identifies the common case and transfer control to fast path code  Extract and optimize fast path code manually  Finish: Patch up state and return control at the end of fast path  Obvious drawbacks  Difficult to implement correctly  Difficult to maintain

8. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs8 Outline  Motivation  Fast Paths  Fast Paths in Concurrent Programs  Experimental Evaluation  Conclusions

9. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs9 Fast Path (Automata-based) Our Approach Baseline (Process-based) Test 1 a = b; b = c * d; d = 0; if (c > 0) c++; a = c; b = c * d; d = 3; if (c > 0) c++; Optimized Code 2 Abort? 3

10. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs10 Specifying Fast Paths  Multiple processes  Concurrent Program  Regular expressions  Statements  Conditions (Optional)  Synchronization (Optional)  Support early abort  Advantages  Powerful  Compact  Hint fastpath example { process first { statement A, B, C, D, #1; start A ? (size<100); follows B ( C D )*; exit #1; } process second {... } process third {... }

11. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs11 Extracting Fast Paths  Automata-based approach to extract fast paths  A Fast Path involves a group of processes  Compiler keeps track of the execution point for each of the involved processes  On exit, control is returned to the appropriate location in each of the processes Baseline: Concurrent. Fast Path: Sequential Code  Fairness on Fast Path  Embed scheduling decisions in the fast path ─ Avoid scheduling/fairness overhead on the fast path  Rely on baseline code for fairness ─ Always taken a fraction of the time

12. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs12 Optimization on Fast Path  Enabling Traditional Fast Paths  Generate and Optimize baseline code  Generate Fast path code ─ Fast Paths have exit/entry points to baseline code  Use data-flow information from baseline code at the exit/entry point to start analysis and optimize the fast path code  Speeding up fast path using lazy execution  Delay operations that are not needed when fast paths are executed to the end  Such operations can be performed if the fast path is aborted

13. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs13 Outline  Motivation  Fast Paths  Fast Paths in Concurrent Programs  Experimental Evaluation  Conclusions

14. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs14 Experimental Evaluation  Implemented the techniques in the paper  In ESP Compiler ─ Supports concurrent programs  Two class of programs  Filter Programs  VMMC Firmware  Answer three questions  Programming effort (annotation complexity) needed  Size of the executable  Performance

15. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs15 Filter Programs  Well-defined structure  Streaming applications  Use Filter Programs by Probsting et al.  Good to evaluate our technique ─ Concurrency overheads dominate  Experimental Setup  2.66 GHz Pentium 4, 1 GB Memory, Linux 2.4  4 Versions of the code  Annotation Complexity  Program sizes: 153, 125, 190, 196 lines  Annotation sizes: 7, 7, 10, 10 lines P1 C1 P2 P3 C2 P4 C3

16. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs16 Filter Programs Cont’d 4.1723.5228.33 9.47 5.15 5.53 Executable Size Performance Program 1 Program 2Program 3Program 4 Better Performance than Both Relatively Small Executable

17. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs17 VMMC Firmware  Firmware for a gigabit network (Myrinet)  Experimental Setup  Measure network performance between two machines connected with Myrinet ─ Latency & Bandwidth  3 Versions of the firmware ─ Concurrent C version with Manual Fast Paths ─ Process-based code without Fast Paths ─ Process-based code with Compiler-extracted Fast Paths  Annotation Complexity (3 fast paths)  Fast Path Specification: 20, 14, and 18 lines  Manual Fast Paths in C: 1100 lines total

18. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs18 VMMC Firmware Cont’d Message size (in Bytes) Performance: Latency ss Generated Code Size Assembly Instructions

19. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs19 Outline  Motivation  Fast Paths  Fast Paths in Concurrent Programs  Experimental Evaluation  Conclusions

20. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs20 Conclusions  Fast Paths in Concurrent Programs  Evaluated using Filter programs and VMMC firmware  Process-based approach to handle all cases  Keeps executable size reasonable  Automata-based approach to handle only the common cases (Fast Path)  Avoid high overhead of process-based approach  Often outperforms the automata-based code

21. Questions ?

22. Intel Labs & Princeton UniversityFast Paths in Concurrent Programs22 ABCDEF Abcdef Ghijk