Performance, Concurrency and Big Data
Bart Massey 2013-11-18
- Misc notes
- Concurrency and Big Data
- Case study
- Daily WTF CSOD is http://thedailywtf.com/Articles/The-New-Line-in-Performance.aspx
Performance Optimization Overview
Usually not important because requirements
- No performance requirement
- Performance requirement easily met
- Performance requirement unattainable
Most changes are not meaningful; almost always looking for minimum 2x
Code tuning is mostly silly; use a better language or compiler
What To Think About For Performance Up Front
Is there a performance requirement? Is it minimal?
Does the architecture look like it can meet the goal?
Does the language / environment have the needed performance?
Are the chosen algorithms likely to be OK?
How will performance be validated?
Performance only after correctness
What To Think About When Fixing A Performance Problem
Can the requirement change?
Where are the bottlenecks?
Can you buy/use hardware to solve the problem?
Is there an algorithm or data structure that can easily change?
Can you recode bottlenecks in a faster language?
Will code tuning help? (default no)
Will paralellism help? (default no)
Remember Memory Doctor
Memory hierarchy has huge performance effect:
- Hard disk (10x)
- Flash disk (10x)
- RAM (10x)
- L2 cache (5x)
- L1 cache (2x)
Remember, paging is disk access
Modern CPUs are scary fast (McConnell notwithstanding)
System Calls Are Not Your Friend
OS is burdened with:
- Lots of bookkeeping
- Expensive protection mechanisms
Only call it when you have to
Buffered I/O is helpful, but not a panacea
- Buffers can't match rates
You Must Profile
You will have performance bottlenecks
You have no idea where your performance bottlenecks are
Profiling is hard
- Slows down program
- Interacts badly with optimization
- Interpretation is hard
Performance Tuning Strategy
Diagnostic activity, like debugging
- Determine proximate cause
- Determine root cause
- Propagate root cause
- Devise repair
- Check repair
Bad performance is a failure, and may indicate underlying bugs
Learn To Use Your Compiler
Optimization flags and options
Concurrency and Big Data
Concurrency is potential parallelism; not always realized
Sequential programs are a million times easier
Costs of concurrency are high
- Big overhead for data management
- Synchronization dramatically reduces speedup
- Shared resources become bottleneck
"True" concurrency vs. naive concurrency
When To Paralellize
- Naive concurrency works, and
- Sharing should help, and
- Developer skill / effort is not a limiting factor
Modern web apps meet these criteria
Optimization Case Study