Initial project scaffold

tasks/07_profiling/spec.md

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+# Task 07: Profiling
+
+## 1. Problem Statement
+
+Profile one kernel at a time and learn to interpret the first few metrics before tuning anything.
+
+## 2. Expected Input/Output Shapes
+
+Use the same shapes as your benchmark harness so measurements stay comparable.
+
+## 3. Performance Intuition
+
+Profiling is how you turn guesses into evidence. Use it after correctness is established.
+
+## 4. Memory Access Discussion
+
+Profilers can tell you whether the kernel is limited by memory throughput, occupancy, or something else. Interpret those numbers in terms of the operator's access pattern.
+
+## 5. What Triton Is Abstracting
+
+Triton hides low-level details in code, but profilers still show the resulting kernels and hardware behavior.
+
+## 6. What CUDA Makes Explicit
+
+CUDA kernels expose their launch shapes, synchronization behavior, and memory hierarchy choices more directly, which can make profiler results easier to map back to code.
+
+## 7. Reflection Questions
+
+- Did you profile a single kernel or an entire script?
+- Did you warm up before timing?
+- Which metric was the first signal that the kernel was bandwidth-bound or compute-bound?
+
+## 8. Implementation Checklist
+
+- Pick one benchmark and one implementation
+- Warm up first
+- Synchronize before and after timing
+- Run `ncu` and inspect a small set of metrics
+- Run `nsys` and inspect the timeline
+- Write down what you learned before changing the kernel