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crates/integration/rtx-rustybooks/src/profiler/warp_analysis.rs

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+//! Warp Analysis
+//!
+//! Provides warp-level (CUDA) and SIMD group (Metal) analysis.
+
+use std::fmt;
+
+/// Warp-level metrics for CUDA kernels
+#[derive(Debug, Clone, Default)]
+pub struct WarpMetrics {
+    /// Number of active warps
+    pub active_warps: u32,
+    /// Maximum theoretical warps
+    pub max_warps: u32,
+    /// Theoretical occupancy based on register/shared memory usage
+    pub theoretical_occupancy: f64,
+    /// Achieved occupancy from actual execution
+    pub achieved_occupancy: f64,
+    /// Warp execution efficiency (% of threads active)
+    pub warp_execution_efficiency: f64,
+    /// Branch divergence ratio (0.0 = no divergence, 1.0 = full divergence)
+    pub branch_divergence: f64,
+    /// Stall cycles per warp
+    pub stall_cycles: u64,
+    /// Instructions per warp
+    pub instructions_per_warp: u64,
+}
+
+impl WarpMetrics {
+    /// Check if warp utilization is good (>75%)
+    pub fn is_efficient(&self) -> bool {
+        self.achieved_occupancy > 0.75 && self.warp_execution_efficiency > 0.9
+    }
+
+    /// Get efficiency score (0.0 - 1.0)
+    pub fn efficiency_score(&self) -> f64 {
+        (self.achieved_occupancy + self.warp_execution_efficiency + (1.0 - self.branch_divergence))
+            / 3.0
+    }
+
+    /// Get recommendations for improving warp utilization
+    pub fn recommendations(&self) -> Vec<String> {
+        let mut recs = Vec::new();
+
+        if self.achieved_occupancy < 0.5 {
+            recs.push(
+                "Low occupancy: Consider reducing register usage or increasing block size"
+                    .to_string(),
+            );
+        }
+
+        if self.warp_execution_efficiency < 0.8 {
+            recs.push(
+                "Low warp efficiency: Check for divergent branches in inner loops".to_string(),
+            );
+        }
+
+        if self.branch_divergence > 0.2 {
+            recs.push(
+                "High branch divergence: Refactor conditional logic to be warp-uniform".to_string(),
+            );
+        }
+
+        if self.stall_cycles > self.instructions_per_warp * 2 {
+            recs.push("High stall rate: Consider memory access pattern optimization".to_string());
+        }
+
+        if recs.is_empty() {
+            recs.push("Kernel is well-optimized!".to_string());
+        }
+
+        recs
+    }
+}
+
+impl fmt::Display for WarpMetrics {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        write!(
+            f,
+            "WarpMetrics {{ warps: {}/{}, occupancy: {:.1}%/{:.1}%, efficiency: {:.1}%, divergence: {:.1}% }}",
+            self.active_warps,
+            self.max_warps,
+            self.achieved_occupancy * 100.0,
+            self.theoretical_occupancy * 100.0,
+            self.warp_execution_efficiency * 100.0,
+            self.branch_divergence * 100.0,
+        )
+    }
+}
+
+/// SIMD group metrics for Metal kernels
+#[derive(Debug, Clone, Default)]
+pub struct SIMDMetrics {
+    /// SIMD width (threads per SIMD group)
+    pub simd_width: u32,
+    /// Number of active SIMD groups
+    pub active_simd_groups: u32,
+    /// Maximum SIMD groups per threadgroup
+    pub max_simd_groups: u32,
+    /// SIMD lane utilization (0.0 - 1.0)
+    pub lane_utilization: f64,
+    /// Threadgroup memory usage (bytes)
+    pub threadgroup_memory: u64,
+    /// Maximum threadgroup memory available
+    pub max_threadgroup_memory: u64,
+    /// Shader execution time (ns)
+    pub shader_time_ns: u64,
+}
+
+impl SIMDMetrics {
+    /// Check if SIMD utilization is efficient
+    pub fn is_efficient(&self) -> bool {
+        self.lane_utilization > 0.75
+    }
+
+    /// Get threadgroup memory utilization
+    pub fn memory_utilization(&self) -> f64 {
+        if self.max_threadgroup_memory == 0 {
+            0.0
+        } else {
+            self.threadgroup_memory as f64 / self.max_threadgroup_memory as f64
+        }
+    }
+
+    /// Get recommendations for improving SIMD efficiency
+    pub fn recommendations(&self) -> Vec<String> {
+        let mut recs = Vec::new();
+
+        if self.lane_utilization < 0.5 {
+            recs.push(
+                "Low lane utilization: Ensure work is evenly distributed across SIMD lanes"
+                    .to_string(),
+            );
+        }
+
+        if self.memory_utilization() > 0.9 {
+            recs.push(
+                "High threadgroup memory pressure: Consider reducing per-thread storage"
+                    .to_string(),
+            );
+        }
+
+        if self.active_simd_groups < self.max_simd_groups / 2 {
+            recs.push("Low SIMD group count: Increase threadgroup size if possible".to_string());
+        }
+
+        if recs.is_empty() {
+            recs.push("SIMD execution is efficient!".to_string());
+        }
+
+        recs
+    }
+}
+
+impl fmt::Display for SIMDMetrics {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        write!(
+            f,
+            "SIMDMetrics {{ width: {}, groups: {}/{}, lane_util: {:.1}%, tg_mem: {}/{} }}",
+            self.simd_width,
+            self.active_simd_groups,
+            self.max_simd_groups,
+            self.lane_utilization * 100.0,
+            self.threadgroup_memory,
+            self.max_threadgroup_memory,
+        )
+    }
+}
+
+/// Warp/SIMD analyzer
+pub struct WarpAnalyzer {
+    /// Device type being analyzed
+    device_type: DeviceType,
+}
+
+#[derive(Debug, Clone, Copy, PartialEq, Eq)]
+pub enum DeviceType {
+    Cuda,
+    Metal,
+    Unknown,
+}
+
+impl WarpAnalyzer {
+    /// Create a new warp analyzer for CUDA
+    pub fn cuda() -> Self {
+        Self {
+            device_type: DeviceType::Cuda,
+        }
+    }
+
+    /// Create a new SIMD analyzer for Metal
+    pub fn metal() -> Self {
+        Self {
+            device_type: DeviceType::Metal,
+        }
+    }
+
+    /// Get the device type
+    pub fn device_type(&self) -> DeviceType {
+        self.device_type
+    }
+
+    /// Analyze warp metrics for a kernel
+    ///
+    /// Note: Actual implementation requires CUPTI or Metal profiling APIs
+    pub fn analyze_warp(&self, _kernel_id: u64) -> WarpMetrics {
+        // Placeholder - actual implementation would query profiling APIs
+        WarpMetrics {
+            active_warps: 32,
+            max_warps: 64,
+            theoretical_occupancy: 0.5,
+            achieved_occupancy: 0.45,
+            warp_execution_efficiency: 0.92,
+            branch_divergence: 0.05,
+            stall_cycles: 1000,
+            instructions_per_warp: 500,
+        }
+    }
+
+    /// Analyze SIMD metrics for a Metal kernel
+    pub fn analyze_simd(&self, _kernel_id: u64) -> SIMDMetrics {
+        // Placeholder - actual implementation would query Metal profiling APIs
+        SIMDMetrics {
+            simd_width: 32,
+            active_simd_groups: 8,
+            max_simd_groups: 16,
+            lane_utilization: 0.88,
+            threadgroup_memory: 16384,
+            max_threadgroup_memory: 32768,
+            shader_time_ns: 1000000,
+        }
+    }
+
+    /// Calculate optimal configuration for a kernel
+    pub fn suggest_config(
+        &self,
+        work_size: usize,
+        registers_per_thread: u32,
+        shared_memory: u64,
+    ) -> KernelConfig {
+        match self.device_type {
+            DeviceType::Cuda => {
+                // CUDA: 32 threads per warp, aim for 50%+ occupancy
+                let threads_per_block = if registers_per_thread > 64 {
+                    128 // Low register pressure tolerance
+                } else if registers_per_thread > 32 {
+                    256
+                } else {
+                    512 // High register efficiency
+                };
+
+                let blocks = (work_size + threads_per_block - 1) / threads_per_block;
+
+                KernelConfig {
+                    threads_per_block: threads_per_block as u32,
+                    blocks: blocks as u32,
+                    shared_memory,
+                }
+            }
+            DeviceType::Metal => {
+                // Metal: SIMD width is typically 32, threadgroup size up to 1024
+                let threads_per_threadgroup = if shared_memory > 16384 { 256 } else { 512 };
+
+                let threadgroups =
+                    (work_size + threads_per_threadgroup - 1) / threads_per_threadgroup;
+
+                KernelConfig {
+                    threads_per_block: threads_per_threadgroup as u32,
+                    blocks: threadgroups as u32,
+                    shared_memory,
+                }
+            }
+            DeviceType::Unknown => KernelConfig {
+                threads_per_block: 256,
+                blocks: ((work_size + 255) / 256) as u32,
+                shared_memory,
+            },
+        }
+    }
+}
+
+/// Suggested kernel launch configuration
+#[derive(Debug, Clone)]
+pub struct KernelConfig {
+    /// Threads per block/threadgroup
+    pub threads_per_block: u32,
+    /// Number of blocks/threadgroups
+    pub blocks: u32,
+    /// Shared/threadgroup memory (bytes)
+    pub shared_memory: u64,
+}
+
+impl KernelConfig {
+    /// Total number of threads
+    pub fn total_threads(&self) -> u64 {
+        self.threads_per_block as u64 * self.blocks as u64
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_warp_metrics_default() {
+        let metrics = WarpMetrics::default();
+        assert_eq!(metrics.active_warps, 0);
+        assert_eq!(metrics.achieved_occupancy, 0.0);
+    }
+
+    #[test]
+    fn test_warp_metrics_efficiency() {
+        let efficient = WarpMetrics {
+            achieved_occupancy: 0.8,
+            warp_execution_efficiency: 0.95,
+            branch_divergence: 0.05,
+            ..Default::default()
+        };
+        assert!(efficient.is_efficient());
+
+        let inefficient = WarpMetrics {
+            achieved_occupancy: 0.3,
+            warp_execution_efficiency: 0.6,
+            ..Default::default()
+        };
+        assert!(!inefficient.is_efficient());
+    }
+
+    #[test]
+    fn test_warp_recommendations() {
+        let low_occupancy = WarpMetrics {
+            achieved_occupancy: 0.3,
+            warp_execution_efficiency: 0.95,
+            ..Default::default()
+        };
+        let recs = low_occupancy.recommendations();
+        assert!(recs.iter().any(|r| r.contains("occupancy")));
+    }
+
+    #[test]
+    fn test_simd_metrics() {
+        let metrics = SIMDMetrics {
+            threadgroup_memory: 8192,
+            max_threadgroup_memory: 32768,
+            lane_utilization: 0.9,
+            ..Default::default()
+        };
+        assert_eq!(metrics.memory_utilization(), 0.25);
+        assert!(metrics.is_efficient());
+    }
+
+    #[test]
+    fn test_kernel_config_suggestion() {
+        let analyzer = WarpAnalyzer::cuda();
+        let config = analyzer.suggest_config(1_000_000, 32, 0);
+        assert!(config.threads_per_block >= 256);
+        assert!(config.total_threads() >= 1_000_000);
+    }
+}