Multi-threaded performance. Pitfalls презентация

Multi-threaded Performance Pitfalls Ciaran McHale

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Purpose of this presentation Some issues in multi-threading are counter-intuitive Ignorance of these issues can result in poor performance Performance can actually get worse when you add more CPUs This presentation explains the counter-intuitive issues

1. A case study

Architectural diagram

Architectural notes The customer felt J2EE was slower than CORBA/C++ So, the architecture had: Multiple J2EE App Servers acting as clients to… Just one CORBA/C++ server that ran on an 8-CPU Solaris box The customer assumed the CORBA/C++ server “should be able to cope with the load”

Strange problems were observed Throughput of the CORBA server decreased as the number of CPUs increased It ran fastest on 1 CPU It ran slower but “fast enough” with moderate load on 4 CPUs (development machines) It ran very slowly on 8 CPUs (production machine) The CORBA server ran faster if a thread pool limit was imposed Under a high load in production: Most requests were processed in < 0.3 second But some took up to a minute to be processed A few took up to 30 minutes to be processed This is not what you hope to see

2. Analysis of the problems

What went wrong? Investigation showed that scalability problems were caused by a combination of: Cache consistency in multi-CPU machines Unfair mutex wakeup semantics These issues are discussed in the following slides Another issue contributed (slightly) to scalability problems: Bottlenecks in application code A discussion of this is outside the scope of this presentation

Cache consistency RAM access is much slower than speed of CPU Solution: high-speed cache memory sits between CPU and RAM Cache memory works great: In a single-CPU machine In a multi-CPU machine if the threads of a process are “bound” to a CPU Cache memory can backfire if the threads in a program are spread over all the CPUs: Each CPU has a separate cache Cache consistency protocol require cache flushes to RAM (cache consistency protocol is driven by calls to lock() and unlock())

Cache consistency (cont’) Overhead of cache consistency protocols worsens as: Overhead of a cache synchronization increases (this increases as the number of CPUs increase) Frequency of cache synchronization increases (this increases with the rate of mutex lock() and unlock() calls) Lessons: Increasing number of CPUs can decrease performance of a server Work around this by: Having multiple server processes instead of just one Binding each process to a CPU (avoids need for cache synchronization) Try to minimize need for mutex lock() and unlock() in application Note: malloc()/free(), and new/delete use a mutex

Unfair mutex wakeup semantics A mutex does not guarantee First In First Out (FIFO) wakeup semantics To do so would prevent two important optimizations (discussed on the following slides) Instead, a mutex provides: Unfair wakeup semantics Can cause temporary starvation of a thread But guarantees to avoid infinite starvation High speed lock() and unlock()

Unfair mutex wakeup semantics (cont’) Why does a mutex not provide fair wakeup semantics? Because most of the time, speed matter more than fairness When FIFO wakeup semantics are required, developers can write a FIFOMutex class and take a performance hit

Mutex optimization 1 Pseudo-code: void lock() { if (rand() % 100) < 98) { add thread to head of list; // LIFO wakeup } else { add thread to tail of list; // FIFO wakeup } } Notes: Last In First Out (LIFO) wakeup increases likelihood of cache hits for the woken-up thread (avoids expense of cache misses) Occasionally putting a thread at the tail of the queue prevents infinite starvation

Mutex optimization 2 Assume several threads concurrently execute the following code: for (i = 0; i < 1000; i++) { lock(a_mutex); process(data[i]); unlock(a_mutex); } A thread context switch is (relatively) expensive Context switching on every unlock() would add a lot of overhead Solution (this is an unfair optimization): Defer context switches until the end of the current thread’s time slice Current thread can repeatedly lock() and unlock() mutex in a single time slice

3. Improving Throughput

Improving throughput 20X increase in throughput was obtained by combination of: Limiting size of the CORBA server’s thread pool This Decreased the maximum length of the mutex wakeup queue Which decreased the maximum wakeup time Using several server processes (each with a small thread pool) rather than one server process (with a very large thread pool) Binding each server process to one CPU This avoided the overhead of cache consistency Binding was achieved with the pbind command on Solaris Windows has an equivalent of process binding: Use the SetProcessAffinityMask() system call Or, in Task Manager, right click on a process and choose the menu option (this menu option is visible only if you have a multi-CPU machine)

4. Finishing up

Recap: architectural diagram

The case study is not an isolated incident The project’s high-level architecture is quite common: Multi-threaded clients communicate with a multi-threaded server Server process is not “bound” to a single CPU Server’s thread pool size is unlimited (this is the default case in many middleware products) Likely that many projects have similar scalability problems: But the system load is not high enough (yet) to trigger problems Problems are not specific to CORBA They are independent of your choice of middleware technology Multi-core CPUs are becoming more common So, expect to see these scalability issues occurring more frequently

Summary: important things to remember Recognize danger signs: Performance drops as number of CPUs increases Wide variation in response times with a high number of threads Good advice for multi-threaded servers: Put a limit on the size of a server’s thread pool Have several server processes with a small number of threads instead of one process with many threads Bind each a server process to a CPU