Profile .NET applications for CPU performance, memory allocations, lock contention, exceptions, heap analysis, and JIT inlining. Use when the user asks about performance bottlenecks, memory leaks, high CPU, slow code, lock contention, excessive exceptions, GC pressure, heap growth, or JIT compilation in .NET projects.
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Profile .NET applications for CPU performance, memory allocations, lock contention, exceptions, heap analysis, and JIT inlining. Use when the user asks about performance bottlenecks, memory leaks, high CPU, slow code, lock contention, excessive exceptions, GC pressure, heap growth, or JIT compilation in .NET projects.
A CLI profiler for .NET that outputs structured text — no GUI needed. Designed for both human inspection and AI-assisted analysis. It wraps dotnet-trace and dotnet-gcdump and presents call trees, hot functions, allocation tables, and contention rankings as plain text.
Results are written to profile-output/ in the current directory.
Running via dnx (no install needed)
dnx asynkron-profiler [flags] -- [target]
On first run, dnx will prompt to download the package.
Profiling Modes
CPU Profiling (--cpu)
Sampled CPU profiling. Shows call trees and hot function tables.
Takes a GC heap snapshot using dotnet-gcdump. Shows retained objects by type and size.
dnx asynkron-profiler --heap -- ./MyApp.csproj
JIT / Inlining Analysis
The profiler can capture JIT-to-native compilation events, showing what methods get JIT compiled and which calls get inlined. This is useful for understanding runtime code generation and verifying that hot paths are being optimized by the JIT.
Analyzing Existing Traces
You can analyze previously captured trace files without re-running the app:
Profiling is iterative. Follow this workflow to systematically identify and eliminate bottlenecks.
Step 1: Build Release First
Always build Release before profiling. Debug builds have disabled optimizations, extra checks, and no inlining — profiling them gives misleading results.
dotnet build -c Release
Step 2: Choose the Right Mode
Symptom
Start with
High CPU / slow execution
--cpu
High memory / GC pressure
--memory
High latency but low CPU
--contention
Too many exceptions in logs
--exception
Memory keeps growing (leak)
--heap
Want to verify JIT optimization
JIT/inlining analysis
Step 3: Read the Hot Function Table
The profiler outputs a hot function table showing where time or allocations are concentrated:
=== HOT FUNCTIONS ===
Time (ms) Calls Function
-------------------------------------------------
38805.39 19533 MyApp.Core.ProcessItem...
19769.23 9897 MyApp.Core.TransformData...
Focus on the top 3-5 entries. These are your optimization targets.
Step 4: Read the Allocation Call Graph
For memory profiling, the allocation call graph shows where allocations originate:
This traces allocations back to their source — the method that triggered them, not just where new was called.
Step 5: Iterate
Profile to find the top bottleneck
Fix it (optimize hot path, reduce allocations, remove contention)
Profile again to measure improvement
Repeat until performance targets are met
Track progress across rounds:
Round 1: 322 MB, 172 ms
Round 2: 173 MB, 150 ms (pooling)
Round 3: 107 MB, 116 ms (fast paths)
Step 6: Use Manual Tracing for Deep Dives
When the profiler summary isn't enough, capture a detailed trace for manual analysis:
# Capture detailed GC trace
dotnet-trace collect \
--profile gc-verbose \
--format NetTrace \
-o trace.nettrace \
-- dotnet run -c Release --project ./MyApp
# Analyze with the profiler
dnx asynkron-profiler --input trace.nettrace --memory
# Or convert for external tools
dotnet-trace convert trace.nettrace --format Speedscope
Common Optimization Patterns
Reduce allocations in hot loops:
Use object pooling for frequently created/disposed objects
Use Span<T> / stackalloc for short-lived buffers
Avoid boxing value types (use generic overloads)
Reduce CPU in hot paths:
Use [MethodImpl(MethodImplOptions.AggressiveInlining)] on small hot methods
Split into fast path (inlined, common case) and slow path (NoInlining, rare case)
Cache computed values instead of recomputing
Reduce contention:
Use lock-free patterns (Interlocked, ConcurrentDictionary)
Reduce lock scope — hold locks for the shortest time possible
Use ReaderWriterLockSlim for read-heavy workloads
Reduce exceptions:
Use TryParse / TryGet patterns instead of catching exceptions
Exceptions are expensive — never use them for control flow
JIT-Aware Optimization Patterns
These patterns work with the .NET JIT compiler to produce faster native code. Use the profiler's JIT/inlining analysis to verify these optimizations take effect.
Fast/Slow Path Split
The most impactful pattern for hot methods. The JIT inlines small methods into their callers, eliminating call overhead and enabling further optimizations. But it won't inline large methods. The trick: keep the common case tiny and inlineable, push the rare case into a separate non-inlined method.
[MethodImpl(MethodImplOptions.AggressiveInlining)]
privatestatic Result HandleHotPath(Data data)
{
// Fast path: ~20-30 lines max, handles the common caseif (data.IsSimpleCase)
{
// Direct, minimal workreturn Result.From(data.Value);
}
// Rare/complex case — delegate to non-inlined methodreturn HandleHotPathSlow(data);
}
[MethodImpl(MethodImplOptions.NoInlining)]
privatestatic Result HandleHotPathSlow(Data data)
{
// Complex logic: type coercion, error handling, edge cases// This can be as large as needed — it won't bloat the call site
}
Why this works:
The JIT inlines the fast path directly into the hot loop
The slow path stays as a regular call — it doesn't bloat the inlined code
CPU branch prediction favors the fast path since it's the common case
Instruction cache stays hot because the loop body is small
When to apply:
Method shows up in profiler hot function table
Method has a common fast case and rare complex case
The fast case is under ~30 lines
The method is called in a tight loop
How to verify: Use the profiler's JIT/inlining analysis to confirm the fast path is being inlined at the call site.
Dispatch Tables vs Switch Statements
For hot dispatch (e.g., instruction interpreters, message handlers, event processors), a delegate array indexed by enum is faster than a switch:
// Define handler signaturedelegate Result Handler(Context ctx, Instruction instr);
// Build dispatch table once (static constructor)privatestaticreadonly Handler[] _dispatch = new Handler[64];
staticMyRunner()
{
_dispatch[(int)Kind.Add] = HandleAdd;
_dispatch[(int)Kind.Call] = HandleCall;
_dispatch[(int)Kind.Branch] = HandleBranch;
// ...
}
// Hot loop — direct delegate invocation, no switch overheadwhile (running)
{
var instr = instructions[pc];
var result = _dispatch[(int)instr.Kind](ctx, instr);
// ...
}
Why this works:
Direct indexed lookup — O(1), no comparisons
Each handler can have its own inlining attributes
Scales better than switch as the number of cases grows
When to apply:
Dispatch over an enum with 10+ cases
Called in a tight loop (interpreter, event loop, message pump)
Switch shows up in profiler as a hot function
Object Pooling for Hot Allocations
When the profiler shows a type being allocated millions of times in a loop, pool it instead.
// 1. Define what poolable objects look likeinterfaceIRentable
{
voidActivate(); // Called when rented — initialize statevoidReset(); // Called when returned — clear state for reuse
}
// 2. Lock-free pool using Interlocked.CompareExchangeclassObjectPool<T> whereT : class, IRentable
{
privatereadonly T?[] _items;
privatereadonly Func<T> _factory;
public T Rent()
{
for (int i = 0; i < _items.Length; i++)
{
var item = Interlocked.Exchange(ref _items[i], null);
if (item isnotnull) { item.Activate(); return item; }
}
var created = _factory();
created.Activate();
return created;
}
publicvoidReturn(T item)
{
item.Reset();
for (int i = 0; i < _items.Length; i++)
{
if (Interlocked.CompareExchange(ref _items[i], item, null) == null)
return;
}
// Pool full — abandon to GC (graceful degradation)
}
}
// 3. RAII wrapper ensures objects are returnedusingvar handle = pool.Rent();
var obj = handle.Value;
// ... use obj ...// Automatically returned on dispose
Impact: A tight loop creating 1M scoped objects goes from 1M allocations to ~32 (pool size). Dramatically reduces GC pressure.
When to apply:
A type shows up in the memory profiler's top allocations
It's created and disposed in a tight loop
Its lifetime is short and predictable
The type can be cleanly reset for reuse
Thread-Safe Lazy Initialization
For cached computed values that are expensive to create but read frequently:
static TCache GetOrCreate<TCache>(ref TCache? field, Func<TCache> factory)
where TCache : class
{
var existing = Volatile.Read(ref field);
if (existing isnotnull) return existing;
var created = factory();
var prior = Interlocked.CompareExchange(ref field, created, null);
return prior ?? created;
}
Why not Lazy<T>: This pattern avoids the Lazy<T> allocation itself, and the Volatile.Read fast path is a single instruction on x86/ARM. The worst case (two threads create simultaneously) wastes one creation but is still correct — no locks needed.
Guidelines
Always build Release first (dotnet build -c Release) before profiling — profiling Debug builds gives misleading results
Profile the compiled binary directly rather than via dotnet run for accurate measurements
Use --root to focus on a specific call path when the tree is too broad
Use --filter to narrow function tables to your own code, excluding framework noise
Use --calltree-sibling-cutoff to hide insignificant branches
For memory issues, start with --memory to find allocation sources, then --heap for retained object analysis
For performance, start with --cpu, then drill into contention if CPU usage is low but latency is high
For exception-heavy apps, use --exception with --exception-type to focus on specific exception categories
Profile iteratively — fix one bottleneck at a time and measure the improvement before moving on
