import { useState, useEffect, useRef } from 'react' const API_BASE = window.location.protocol === 'file:' ? 'http://localhost:8000' : window.location.origin // ─── Global CSS ─────────────────────────────────────────────────────────────── const globalCSS = ` @import url('https://fonts.bunny.net/css?family=clash-display:400,500,600,700'); * { cursor: none !important; box-sizing: border-box; } body { background: #080808; margin: 0; } ::-webkit-scrollbar { width: 4px; height: 4px; } ::-webkit-scrollbar-track { background: #080808; } ::-webkit-scrollbar-thumb { background: #1a1a1a; border-radius: 2px; } ::-webkit-scrollbar-thumb:hover { background: #b8ff57; } .clash-display { font-family: 'Clash Display', sans-serif; } @keyframes probe-blink { 0%,49%{opacity:1} 50%,100%{opacity:0} } @keyframes badge-spin { to { transform: rotate(360deg) } } @keyframes shine-sweep { from{background-position:200% 0} to{background-position:-200% 0} } @keyframes slide-up { from{transform:translateY(100%);opacity:0} to{transform:translateY(0);opacity:1} } @keyframes rocm-pulse { 0%,100%{opacity:1} 50%{opacity:0.2} } @keyframes nav-dot-pulse { 0%,100%{opacity:1;box-shadow:0 0 6px #ff4d00} 50%{opacity:0.5;box-shadow:0 0 2px #ff4d00} } @keyframes seg-glow { 0%,100%{box-shadow:4px 0 10px rgba(255,77,0,0.6)} 50%{box-shadow:4px 0 18px rgba(255,77,0,0.9)} } .benchmark-footer { animation: slide-up 300ms ease forwards; } ` // ─── Template Kernels ───────────────────────────────────────────────────────── const KERNEL_VECTOR_ADD = String.raw` #include #include // Vector addition kernel with intentional warp size bug __global__ void vectorAdd(const float *A, const float *B, float *C, int numElements) { int i = blockDim.x * blockIdx.x + threadIdx.x; if (i < numElements) { C[i] = A[i] + B[i]; // Intentional warp size bug - assumes 32 threads per warp // This will break on AMD wavefront (64 threads) if (threadIdx.x % 32 == 0) { // This synchronization only works for CUDA's 32-thread warps printf("Thread %d in warp %d completed\n", threadIdx.x, threadIdx.x / 32); } } } int main(void) { int numElements = 50000; size_t size = numElements * sizeof(float); // Allocate host memory float *h_A = (float *)malloc(size); float *h_B = (float *)malloc(size); float *h_C = (float *)malloc(size); // Initialize host vectors for (int i = 0; i < numElements; ++i) { h_A[i] = rand() / (float)RAND_MAX; h_B[i] = rand() / (float)RAND_MAX; } // Allocate device memory float *d_A, *d_B, *d_C; cudaMalloc((void **)&d_A, size); cudaMalloc((void **)&d_B, size); cudaMalloc((void **)&d_C, size); // Copy data from host to device cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice); cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice); // Launch kernel int threadsPerBlock = 256; int blocksPerGrid = (numElements + threadsPerBlock - 1) / threadsPerBlock; printf("Launching kernel with %d blocks of %d threads\n", blocksPerGrid, threadsPerBlock); vectorAdd<<>>(d_A, d_B, d_C, numElements); cudaDeviceSynchronize(); // Copy result back to host cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost); // Verify result for (int i = 0; i < numElements; ++i) { if (fabs(h_A[i] + h_B[i] - h_C[i]) > 1e-5) { printf("Test FAILED at element %d!\n", i); break; } } printf("Test PASSED\n"); // Free device memory cudaFree(d_A); cudaFree(d_B); cudaFree(d_C); // Free host memory free(h_A); free(h_B); free(h_C); printf("Done\n"); return 0; } `.trim() const KERNEL_MATRIX_MULTIPLY = String.raw` #include #include #include // Matrix multiplication kernel with intentional warp size bug // C = A * B // A: M x K, B: K x N, C: M x N __global__ void matrixMultiply(const float *A, const float *B, float *C, int M, int N, int K) { int row = blockIdx.y * blockDim.y + threadIdx.y; int col = blockIdx.x * blockDim.x + threadIdx.x; if (row < M && col < N) { float sum = 0.0f; for (int k = 0; k < K; ++k) { sum += A[row * K + k] * B[k * N + col]; } C[row * N + col] = sum; // Intentional warp size bug - assumes 32 threads per warp // This will cause incorrect behavior on AMD wavefront (64 threads) if (threadIdx.x % 32 == 0 && threadIdx.y % 32 == 0) { // This warp-level synchronization only works for CUDA printf("Block (%d,%d) warp (%d,%d) computed element (%d,%d) = %f\n", blockIdx.x, blockIdx.y, threadIdx.x / 32, threadIdx.y / 32, row, col, sum); } } } // Optimized version with shared memory (for comparison) __global__ void matrixMultiplyShared(const float *A, const float *B, float *C, int M, int N, int K) { __shared__ float tileA[32][32]; __shared__ float tileB[32][32]; int row = blockIdx.y * blockDim.y + threadIdx.y; int col = blockIdx.x * blockDim.x + threadIdx.x; float sum = 0.0f; for (int tile = 0; tile < (K + 31) / 32; ++tile) { if (row < M && tile * 32 + threadIdx.x < K) { tileA[threadIdx.y][threadIdx.x] = A[row * K + tile * 32 + threadIdx.x]; } else { tileA[threadIdx.y][threadIdx.x] = 0.0f; } if (col < N && tile * 32 + threadIdx.y < K) { tileB[threadIdx.y][threadIdx.x] = B[(tile * 32 + threadIdx.y) * N + col]; } else { tileB[threadIdx.y][threadIdx.x] = 0.0f; } __syncthreads(); for (int k = 0; k < 32; ++k) { sum += tileA[threadIdx.y][k] * tileB[k][threadIdx.x]; } __syncthreads(); } if (row < M && col < N) { C[row * N + col] = sum; } } int main(int argc, char **argv) { int M = 512, N = 512, K = 512; size_t size_A = M * K * sizeof(float); size_t size_B = K * N * sizeof(float); size_t size_C = M * N * sizeof(float); float *h_A = (float *)malloc(size_A); float *h_B = (float *)malloc(size_B); float *h_C = (float *)malloc(size_C); float *h_C_ref = (float *)malloc(size_C); for (int i = 0; i < M * K; ++i) h_A[i] = rand() / (float)RAND_MAX; for (int i = 0; i < K * N; ++i) h_B[i] = rand() / (float)RAND_MAX; float *d_A, *d_B, *d_C, *d_C_ref; cudaMalloc(&d_A, size_A); cudaMalloc(&d_B, size_B); cudaMalloc(&d_C, size_C); cudaMalloc(&d_C_ref, size_C); cudaMemcpy(d_A, h_A, size_A, cudaMemcpyHostToDevice); cudaMemcpy(d_B, h_B, size_B, cudaMemcpyHostToDevice); dim3 threadsPerBlock(32, 32); dim3 blocksPerGrid((N + threadsPerBlock.x - 1) / threadsPerBlock.x, (M + threadsPerBlock.y - 1) / threadsPerBlock.y); printf("Matrix dimensions: %dx%d * %dx%d = %dx%d\n", M, K, K, N, M, N); printf("Launching kernel with grid (%d,%d) and block (%d,%d)\n", blocksPerGrid.x, blocksPerGrid.y, threadsPerBlock.x, threadsPerBlock.y); // Warmup matrixMultiply<<>>(d_A, d_B, d_C_ref, M, N, K); cudaDeviceSynchronize(); cudaEvent_t start, stop; cudaEventCreate(&start); cudaEventCreate(&stop); cudaEventRecord(start); matrixMultiply<<>>(d_A, d_B, d_C_ref, M, N, K); cudaEventRecord(stop); cudaEventSynchronize(stop); float basic_time = 0; cudaEventElapsedTime(&basic_time, start, stop); printf("Basic kernel time: %.3f ms\n", basic_time); cudaEventRecord(start); matrixMultiplyShared<<>>(d_A, d_B, d_C, M, N, K); cudaEventRecord(stop); cudaEventSynchronize(stop); float shared_time = 0; cudaEventElapsedTime(&shared_time, start, stop); printf("Shared memory kernel time: %.3f ms\n", shared_time); printf("Speedup: %.2fx\n", basic_time / shared_time); cudaMemcpy(h_C_ref, d_C_ref, size_C, cudaMemcpyDeviceToHost); cudaMemcpy(h_C, d_C, size_C, cudaMemcpyDeviceToHost); bool correct = true; for (int i = 0; i < M * N; ++i) { if (fabs(h_C[i] - h_C_ref[i]) > 1e-5) { printf("Mismatch at element %d: %f != %f\n", i, h_C[i], h_C_ref[i]); correct = false; break; } } printf(correct ? "Verification PASSED\n" : "Verification FAILED\n"); cudaFree(d_A); cudaFree(d_B); cudaFree(d_C); cudaFree(d_C_ref); free(h_A); free(h_B); free(h_C); free(h_C_ref); printf("Done\n"); return 0; } `.trim() const KERNEL_CONVOLUTION_2D = String.raw` #include #include #include // 2D Convolution kernel with intentional warp size bug __global__ void convolution2D(const float *input, const float *kernel, float *output, int input_height, int input_width, int kernel_size, int output_height, int output_width) { int row = blockIdx.y * blockDim.y + threadIdx.y; int col = blockIdx.x * blockDim.x + threadIdx.x; if (row < output_height && col < output_width) { float sum = 0.0f; int kernel_radius = kernel_size / 2; for (int i = -kernel_radius; i <= kernel_radius; i++) { for (int j = -kernel_radius; j <= kernel_radius; j++) { int input_row = row + i; int input_col = col + j; if (input_row >= 0 && input_row < input_height && input_col >= 0 && input_col < input_width) { int kernel_row = i + kernel_radius; int kernel_col = j + kernel_radius; sum += input[input_row * input_width + input_col] * kernel[kernel_row * kernel_size + kernel_col]; } } } output[row * output_width + col] = sum; // Intentional warp size bug - assumes 32 threads per warp // This will break on AMD wavefront (64 threads) if (threadIdx.x % 32 == 0 && threadIdx.y % 32 == 0) { printf("Warp (%d,%d) processed output pixel (%d,%d) = %f\n", threadIdx.x / 32, threadIdx.y / 32, row, col, sum); } } } // Shared memory version for comparison __global__ void convolution2DShared(const float *input, const float *kernel, float *output, int input_height, int input_width, int kernel_size, int output_height, int output_width) { __shared__ float shared_input[32 + 6][32 + 6]; // +6 for 3x3 kernel padding __shared__ float shared_kernel[7][7]; // Max 7x7 kernel int row = blockIdx.y * blockDim.y + threadIdx.y; int col = blockIdx.x * blockDim.x + threadIdx.x; int kernel_radius = kernel_size / 2; if (threadIdx.x < kernel_size && threadIdx.y < kernel_size) { shared_kernel[threadIdx.y][threadIdx.x] = kernel[threadIdx.y * kernel_size + threadIdx.x]; } int input_row = blockIdx.y * blockDim.y + threadIdx.y - kernel_radius; int input_col = blockIdx.x * blockDim.x + threadIdx.x - kernel_radius; if (input_row >= 0 && input_row < input_height && input_col >= 0 && input_col < input_width) { shared_input[threadIdx.y][threadIdx.x] = input[input_row * input_width + input_col]; } else { shared_input[threadIdx.y][threadIdx.x] = 0.0f; } __syncthreads(); if (row < output_height && col < output_width) { float sum = 0.0f; for (int i = 0; i < kernel_size; i++) for (int j = 0; j < kernel_size; j++) sum += shared_input[threadIdx.y + i][threadIdx.x + j] * shared_kernel[i][j]; output[row * output_width + col] = sum; } } int main(int argc, char **argv) { int input_height = 1024, input_width = 1024, kernel_size = 3; int output_height = input_height - kernel_size + 1; int output_width = input_width - kernel_size + 1; size_t input_size = input_height * input_width * sizeof(float); size_t kernel_size_bytes = kernel_size * kernel_size * sizeof(float); size_t output_size = output_height * output_width * sizeof(float); printf("Input: %dx%d, Kernel: %dx%d, Output: %dx%d\n", input_height, input_width, kernel_size, kernel_size, output_height, output_width); float *h_input = (float *)malloc(input_size); float *h_kernel = (float *)malloc(kernel_size_bytes); float *h_output = (float *)malloc(output_size); float *h_output_ref = (float *)malloc(output_size); for (int i = 0; i < input_height * input_width; i++) h_input[i] = rand() / (float)RAND_MAX; float kernel_3x3[9] = {-1, -1, -1, -1, 8, -1, -1, -1, -1}; for (int i = 0; i < kernel_size * kernel_size; i++) h_kernel[i] = kernel_3x3[i]; float *d_input, *d_kernel, *d_output, *d_output_ref; cudaMalloc(&d_input, input_size); cudaMalloc(&d_kernel, kernel_size_bytes); cudaMalloc(&d_output, output_size); cudaMalloc(&d_output_ref, output_size); cudaMemcpy(d_input, h_input, input_size, cudaMemcpyHostToDevice); cudaMemcpy(d_kernel, h_kernel, kernel_size_bytes, cudaMemcpyHostToDevice); dim3 threadsPerBlock(32, 32); dim3 blocksPerGrid((output_width + threadsPerBlock.x - 1) / threadsPerBlock.x, (output_height + threadsPerBlock.y - 1) / threadsPerBlock.y); printf("Launching kernel with grid (%d,%d) and block (%d,%d)\n", blocksPerGrid.x, blocksPerGrid.y, threadsPerBlock.x, threadsPerBlock.y); // Warmup convolution2D<<>>( d_input, d_kernel, d_output_ref, input_height, input_width, kernel_size, output_height, output_width); cudaDeviceSynchronize(); cudaEvent_t start, stop; cudaEventCreate(&start); cudaEventCreate(&stop); cudaEventRecord(start); convolution2D<<>>( d_input, d_kernel, d_output_ref, input_height, input_width, kernel_size, output_height, output_width); cudaEventRecord(stop); cudaEventSynchronize(stop); float basic_time = 0; cudaEventElapsedTime(&basic_time, start, stop); printf("Basic kernel time: %.3f ms\n", basic_time); cudaEventRecord(start); convolution2DShared<<>>( d_input, d_kernel, d_output, input_height, input_width, kernel_size, output_height, output_width); cudaEventRecord(stop); cudaEventSynchronize(stop); float shared_time = 0; cudaEventElapsedTime(&shared_time, start, stop); printf("Shared memory kernel time: %.3f ms\n", shared_time); printf("Speedup: %.2fx\n", basic_time / shared_time); cudaMemcpy(h_output_ref, d_output_ref, output_size, cudaMemcpyDeviceToHost); cudaMemcpy(h_output, d_output, output_size, cudaMemcpyDeviceToHost); bool correct = true; for (int i = 0; i < 100 && i < output_height * output_width; i++) { if (fabs(h_output[i] - h_output_ref[i]) > 1e-5) { printf("Mismatch at element %d: %f != %f\n", i, h_output[i], h_output_ref[i]); correct = false; break; } } printf(correct ? "Verification PASSED (first 100 elements)\n" : "Verification FAILED\n"); cudaFree(d_input); cudaFree(d_kernel); cudaFree(d_output); cudaFree(d_output_ref); free(h_input); free(h_kernel); free(h_output); free(h_output_ref); printf("Done\n"); return 0; } `.trim() const KERNEL_REDUCTION = String.raw` #include #include // compile: hipcc -arch=sm_60 -nocudalib reduction.cu // --- IDE & COMPILER COMPATIBILITY LAYER --- #if !defined(__CUDACC__) && !defined(__HIPCC__) #define __global__ #define __shared__ #define __syncthreads() struct dim3 { int x, y, z; dim3(int _x = 1, int _y = 1, int _z = 1) : x(_x), y(_y), z(_z) {} }; typedef unsigned int cudaError_t; typedef void* cudaStream_t; dim3 threadIdx, blockIdx, blockDim; int warpSize = 64; #define cudaMalloc(p, s) (0) #define cudaFree(p) (0) #define cudaMemcpy(d, s, n, k) (0) #define cudaMemcpyHostToDevice 1 #define cudaMemcpyDeviceToHost 2 #define cudaSuccess 0 #define cudaDeviceSynchronize() (0) #define LAUNCH_REDUCTION(g, b, m, ...) reduction_kernel(__VA_ARGS__) #else #define LAUNCH_REDUCTION(g, b, m, ...) reduction_kernel<<>>(__VA_ARGS__) #endif // ------------------------------------------ // Standard reduction template (first pass: block-level) __global__ void reduction_kernel(float* g_idata, float* g_odata, unsigned int n) { extern __shared__ float sdata[]; unsigned int tid = threadIdx.x; unsigned int i = blockIdx.x * (blockDim.x * 2) + threadIdx.x; float mySum = (i < n) ? g_idata[i] : 0; if (i + blockDim.x < n) mySum += g_idata[i + blockDim.x]; sdata[tid] = mySum; __syncthreads(); for (unsigned int s = blockDim.x / 2; s > 32; s >>= 1) { if (tid < s) { sdata[tid] = mySum = mySum + sdata[tid + s]; } __syncthreads(); } // DELIBERATE WARP-SIZE BUG: Assuming warpSize=32 for final unrolled reduction // This will produce incorrect results on AMD (warpSize=64) if (tid < 32) { volatile float* vsmem = sdata; vsmem[tid] = mySum = mySum + vsmem[tid + 32]; vsmem[tid] = mySum = mySum + vsmem[tid + 16]; vsmem[tid] = mySum = mySum + vsmem[tid + 8]; vsmem[tid] = mySum = mySum + vsmem[tid + 4]; vsmem[tid] = mySum = mySum + vsmem[tid + 2]; vsmem[tid] = mySum = mySum + vsmem[tid + 1]; } if (tid == 0) g_odata[blockIdx.x] = sdata[0]; } int main() { const int N = 1048576; // 1M elements const int threadsPerBlock = 256; const int blocksPerGrid = (N + (threadsPerBlock * 2) - 1) / (threadsPerBlock * 2); float *h_input = (float*)malloc(N * sizeof(float)); float *h_output = (float*)malloc(blocksPerGrid * sizeof(float)); for (int i = 0; i < N; i++) h_input[i] = 1.0f; float *d_input, *d_output; cudaMalloc(&d_input, N * sizeof(float)); cudaMalloc(&d_output, blocksPerGrid * sizeof(float)); cudaMemcpy(d_input, h_input, N * sizeof(float), cudaMemcpyHostToDevice); LAUNCH_REDUCTION(blocksPerGrid, threadsPerBlock, threadsPerBlock * sizeof(float), d_input, d_output, N); cudaMemcpy(h_output, d_output, blocksPerGrid * sizeof(float), cudaMemcpyDeviceToHost); float gpu_sum = 0; for (int i = 0; i < blocksPerGrid; i++) gpu_sum += h_output[i]; float cpu_sum = (float)N; printf("Parallel Reduction (1M elements)\n"); printf("CPU Sum: %.1f\n", cpu_sum); printf("GPU Sum: %.1f\n", gpu_sum); printf("Result: %s\n", (gpu_sum == cpu_sum) ? "PASS" : "FAIL (Warp size issue suspected)"); cudaFree(d_input); cudaFree(d_output); free(h_input); free(h_output); return 0; } `.trim() // ─── Constants ──────────────────────────────────────────────────────────────── const TEMPLATES = { 'Vector addition': KERNEL_VECTOR_ADD, 'Matrix multiplication': KERNEL_MATRIX_MULTIPLY, '2D convolution': KERNEL_CONVOLUTION_2D, 'Parallel reduction': KERNEL_REDUCTION, } const AGENT_LIST = ['analyzer', 'translator', 'optimizer', 'tester', 'coordinator'] const AGENT_LABEL = { analyzer: 'ANALYZER', translator: 'TRANSLATOR', optimizer: 'OPTIMIZER', tester: 'TESTER', coordinator: 'COORDINATOR', } const STATUS = { idle: { dot: '#1a1a1a', label: 'IDLE', borderLeft: '2px solid #1a1a1a', cardShadow: 'none', badgeColor: null, }, running: { dot: '#ff4d00', label: 'RUNNING', borderLeft: '2px solid #ff4d00', cardShadow: '-4px 0 12px rgba(255,77,0,0.15)', badgeColor: 'orange', }, done: { dot: '#b8ff57', label: 'DONE', borderLeft: '2px solid #b8ff57', cardShadow: 'none', badgeColor: 'green', }, failed: { dot: '#ff3366', label: 'FAILED', borderLeft: '2px solid #ff3366', cardShadow: 'none', badgeColor: 'coral', }, } const INITIAL_AGENTS = Object.fromEntries( AGENT_LIST.map(a => [a, { status: 'idle', message: 'Waiting\u2026', detail: '' }]) ) // ─── Component 1: ROCmCursor ────────────────────────────────────────────────── const BRACKET_STYLES = ` [data-bracket-state="default"] .bkt-corner { border-color: rgba(184,255,87,0.7); } [data-bracket-state="default"] .bkt-tl { top:0; left:0; } [data-bracket-state="default"] .bkt-tr { top:0; right:0; } [data-bracket-state="default"] .bkt-bl { bottom:0; left:0; } [data-bracket-state="default"] .bkt-br { bottom:0; right:0; } [data-bracket-state="button"] .bkt-corner { border-color: rgba(255,77,0,1); } [data-bracket-state="button"] .bkt-tl { top:6px; left:6px; } [data-bracket-state="button"] .bkt-tr { top:6px; right:6px; } [data-bracket-state="button"] .bkt-bl { bottom:6px; left:6px; } [data-bracket-state="button"] .bkt-br { bottom:6px; right:6px; } [data-bracket-state="input"] .bkt-corner { border-color: rgba(179,232,255,0.7); } [data-bracket-state="input"] .bkt-tl { top:0; left:0; } [data-bracket-state="input"] .bkt-tr { top:0; right:0; } [data-bracket-state="input"] .bkt-bl { bottom:0; left:0; } [data-bracket-state="input"] .bkt-br { bottom:0; right:0; } [data-bracket-state="running"] .bkt-corner { border-color: rgba(255,77,0,0.9); } [data-bracket-state="running"] .bkt-tl { top:0; left:0; } [data-bracket-state="running"] .bkt-tr { top:0; right:0; } [data-bracket-state="running"] .bkt-bl { bottom:0; left:0; } [data-bracket-state="running"] .bkt-br { bottom:0; right:0; } .bkt-corner { position: absolute; width: 8px; height: 8px; transition: top 180ms ease, left 180ms ease, right 180ms ease, bottom 180ms ease, border-color 150ms ease; } .bkt-tl { border-top: 1.5px solid; border-left: 1.5px solid; border-bottom: 1.5px solid transparent; border-right: 1.5px solid transparent; } .bkt-tr { border-top: 1.5px solid; border-right: 1.5px solid; border-bottom: 1.5px solid transparent; border-left: 1.5px solid transparent; } .bkt-bl { border-bottom: 1.5px solid; border-left: 1.5px solid; border-top: 1.5px solid transparent; border-right: 1.5px solid transparent; } .bkt-br { border-bottom: 1.5px solid; border-right: 1.5px solid; border-top: 1.5px solid transparent; border-left: 1.5px solid transparent; } ` function ROCmCursor({ running }) { const dotRef = useRef(null) const boxRef = useRef(null) const mouseRef = useRef({ x: -200, y: -200 }) const lerpRef = useRef({ x: -200, y: -200 }) const rafRef = useRef(null) const targetTypeRef = useRef('default') const runningRef = useRef(running) useEffect(() => { runningRef.current = running }, [running]) useEffect(() => { const onMove = (e) => { mouseRef.current = { x: e.clientX, y: e.clientY } if (dotRef.current) { dotRef.current.style.left = (e.clientX - 4) + 'px' dotRef.current.style.top = (e.clientY - 9) + 'px' } } const onOver = (e) => { const el = e.target if (el.closest('button, [role=button]')) { targetTypeRef.current = 'button' } else if (el.closest('textarea, input')) { targetTypeRef.current = 'input' } else { targetTypeRef.current = 'default' } } window.addEventListener('mousemove', onMove) window.addEventListener('mouseover', onOver) const loop = () => { const lx = lerpRef.current.x + (mouseRef.current.x - lerpRef.current.x) * 0.10 const ly = lerpRef.current.y + (mouseRef.current.y - lerpRef.current.y) * 0.10 lerpRef.current = { x: lx, y: ly } if (boxRef.current) { boxRef.current.style.left = (lx - 18) + 'px' boxRef.current.style.top = (ly - 18) + 'px' const state = runningRef.current ? 'running' : targetTypeRef.current boxRef.current.setAttribute('data-bracket-state', state) } rafRef.current = requestAnimationFrame(loop) } rafRef.current = requestAnimationFrame(loop) return () => { window.removeEventListener('mousemove', onMove) window.removeEventListener('mouseover', onOver) cancelAnimationFrame(rafRef.current) } }, [running]) const blinkDuration = running ? '0.5s' : '1s' const dotColor = running ? '#ff4d00' : '#b8ff57' return ( <>