Writing faster managed code Claudio Caldato Program Manager CLR Performance Team

Outline Performance engineering Performance engineering Managed code performance Managed code performance –Contrast with native code –Garbage collection –Features’ cost and Pitfalls Perf problems diagnosis Perf problems diagnosis

Performance engineering Set goals Set goals Measure, measure and then measure Measure, measure and then measure Know your platform Know your platform Process: Process: –Budget, plan, verify –continuous improvement Measure, track, refine Measure, track, refine automated tests automated tests Build a performance culture Build a performance culture –User expectations –Developer attitudes – perf is my feature!

Moving to managed code Why? Productivity and quality Why? Productivity and quality –Do more with less code –Fewer bugs –Clean, modern libraries targeting modern requirements –Better software, sooner But is performance a problem? But is performance a problem? –Easier to write programs fast –Easier to write fast programs?

Know Your Garbage Collector Why? Why? GC basics GC basics –Pause threads; trace reachable objects from roots; compact live objects; recycle dead memory Self tuning Self tuning CLR is a generational mark and sweep GC CLR is a generational mark and sweep GC

Generation 1Generation 0 New objects allocated in generation 0 GC: accessible references keep objects alive GC: preserves / compacts referenced objects GC: objects left merged into older generation Once again, new objects allocated in generation 0 Garbage Collection in Action

Know Your Garbage Collector GC basics GC basics –Pause threads; trace reachable objects from roots; compact live objects; recycle dead memory Self tuning Self tuning Generational GC Heaps Generational GC Heaps –Gen0 – new objects – cache conscious; fast GC –Gen1 – objects survived a GC of gen0 –Gen2 – long lived objects – survived a GC of gen1,2 –Large object heap Server GC Server GC –Optimized for throughput and multi processor scalability

Garbage Collection Pitfalls Object lifetimes still matter! Object lifetimes still matter! Use an efficient “allocation profile” Use an efficient “allocation profile” –Short lived objects are cheap (but not free) –Don’t have a “midlife crisis” (avoid gen2 churn) –Review with perfmon counters, CLRProfiler Common Pitfalls Common Pitfalls –Keeping refs to “dead” object graphs Null out object references (where appropriate) Null out object references (where appropriate) –Implicit boxing –Pinning young objects –GC.Collect considered harmful –Finalization...

Garbage Collection Pitfalls (2) Finalization and the Dispose Pattern ~C() : non-deterministic clean up. ~C() : non-deterministic clean up. –object unref’d –promote to the next generation –queue finalizer –Costs: retains object graph, finalizer thread use Dispose Pattern use Dispose Pattern –Implement IDisposable –Call GC.SuppressFinalize –Hold few obj fields –Dispose early, when possible use ‘using’ (C#)

Pitfall: Indiscriminate Code Reuse Your choices determine your perf Your choices determine your perf –Your architecture, algorithms,... –Your uses of.NET FX types and methods No specific advice holds everywhere, so you have to do your homework No specific advice holds everywhere, so you have to do your homework –Measure, inspect the time and space costs of your platform(s), in your setting

Data locality Data locality –Remote, disk, RAM, cache –GC: objects allocated together in time, stay together in space Data representation Data representation –Complex data structures with a lot of pointers is GC cost Data Cost

Reflection Cost Fast and Light APIs: Fast and Light APIs: – TypeOf, object.GetType, get_Module, get_MemberType, new Token/Handle resolution APIs Costly APIs: Costly APIs: –MemberInfo, MethodInfo, FieldInfo, GetCustomAttribute, InvokeMember, Invoke, get_Name Only request what you need Only request what you need –minimize the use of GetMembers, GetConstructors, … Consider using the new Token/Handle resolution APIs Consider using the new Token/Handle resolution APIs

Cache members after having retrieved them Cache members after having retrieved them –For instance cache Member’s handle Avoid using Type.InvokeMember Avoid using Type.InvokeMember Avoid doing case insensitive member lookups Avoid doing case insensitive member lookups Use BindingsFlags.ExactMatch whenever possible Use BindingsFlags.ExactMatch whenever possible Use FxCop Use FxCop Insidious Insidious –.NET FX code that uses reflection –Late bound code in VB.NET, JScript.NET Enforce early binding Enforce early binding – Option Explicit On Option Strict On Reflection Cost (2)

P/Invoke, COM Interop Cost Efficient, but frequent calls add up Efficient, but frequent calls add up Costs also depend on marshaling Costs also depend on marshaling –Primitive types and arrays of same are cheap –Unicode to ANSI string conversions are not. Diagnosis Diagnosis –Perfmon:.NET CLR Interop counters (# of marshalling) –Time based profiling Mitigate interop call costs by batching calls or move the boundary Mitigate interop call costs by batching calls or move the boundary

Deployment considerations Assemblies Assemblies –Performance-wise: the fewer, the better! Use GAC Use GAC –Avoids repetitive SN signature verification Use NGEN Use NGEN –Caches pre-JIT’d DLL; code may run slower –Generally reduces startup time, improves code shareability –Try it and measure for yourself

Xml: It is not always the answer System.Xml.dll is 2MB System.Xml.dll is 2MB –Load it only when you need it Don’t use XML classes for trivial tasks Don’t use XML classes for trivial tasks –MyApp.Config.Xml: <MyApp> <Top>512</Top><Left>340</Left> </MyApp>

Analyzing Performance Problems Code Inspection Ildasm – findstr “box” Ildasm – findstr “box” Debuggers – Module loads, rebasing Debuggers – Module loads, rebasing FxCop – Static Analyzer FxCop – Static Analyzer

Analyzing Performance Problems Measure It, With Tools High level diagnostics High level diagnostics –Taskmgr, perfmon, vadump, event tracing for Windows (ETW) Space Space –CLR Profiler, code profilers, ETW Time Time –Code profilers, timing loops, ETW

Improve startup time Cold startup is typically dominated by disk accesses and warm startup by CPU usage. Cold startup is typically dominated by disk accesses and warm startup by CPU usage. Reduce dlls loaded at startup if possible. Reduce dlls loaded at startup if possible. Ngen your assemblies. Jitting consumes CPU at startup. Ngen your assemblies. Jitting consumes CPU at startup. Place strong named assemblies in the GAC. Place strong named assemblies in the GAC. The application would be doing its own computations. Is the bottleneck here? The application would be doing its own computations. Is the bottleneck here?

Resources Patterns & Practices: Improving.NET Application Performance and Scalability Patterns & Practices: Improving.NET Application Performance and Scalability –[ framework developer center.NET framework developer center –Programming Information/performance Usergroup: Usergroup: –microsoft.public.dotnet.framework.performance Blogs Blogs –RicoM, MaoniS Claudio’s Quick list Claudio’s Quick list

Q&A

Questions In the product cycle, when do you start working on performance? In the product cycle, when do you start working on performance? What are the top issues you have to deal with? What are the top issues you have to deal with? Are there good tools to do performance analysis?, what is missing? Are there good tools to do performance analysis?, what is missing? Where type of resources do you use to find answers to performance issues? Where type of resources do you use to find answers to performance issues? Comment the following statement: “In managed code it is easier to find and solve performance issues” Comment the following statement: “In managed code it is easier to find and solve performance issues”