1. Timothy Zhu and Huapeng Zhou
Critical Section Characterization and Acceleration in Real World Applications
Timothy Zhu and Huapeng Zhou

2. Motivation
Performance of multithreaded applications is limited by critical sections
We provide an analytical model to analyze the impact of critical sections on performance

3. Model Theoretical Bounds E[R] ≥ max(D, N*Dmax – E[Z])
N = number of threads (e.g. 4)
R = time waiting on and executing critical section
Z = time executing non-critical section
X = throughput = number of iterations around loop per sec
Theoretical Bounds
E[R] ≥ max(D, N*Dmax – E[Z])
X ≤ min(N/(D+E[Z]), 1/Dmax)
Dmax is the maximum duration of executing a critical section
D is the sum of durations executing critical sections

4. Methodology Implemented a hooking library around pthread
Interpose common mutex and condition variable calls
We also experiment with an alternative spinlock implementation to lower latency
Raw measurements
mutex address, thread id, return address, start time, lock time, unlock time
Benchmarks
memcached
MySQL (oltp-simple, oltp-complex, oltp-nontrx)
Runs on dual socket 6-core Xeon processors (with HT OS sees 24 cores) with 48 Gb RAM

5. Experimental Results I
Memcached has one bottleneck critical section with demand Dmax
E[R] increases as N increases since more threads are waiting
Overall throughput is also affected by other shared resources N
E[R]
E[Z] X D
Dmax 2
1020
8739
867
767 3
2174
7697
1373
1197 5
4485
7117
1668
1359 7
7710
7462
1792
1439 11
21668
9878
2385
1876 13
28092
12118
2527
2005 21
61033
19086
3311
2570

6. Experimental Results II
The operation point falls within theoretical bounds

7. Next Steps
We have a method of identifying a bottleneck critical section
Develop a data-driven simulator
Manipulates logged critical section data
Provides insights on what can be improved
Evaluate architecture ideas
Accelerated Critical Sections (ACS)
Scheduling algorithms