""" Handles parallel distribution of operations across multiple GPUs at electron speed. Implements advanced workload distribution strategies with NVLink topology awareness. """ import numpy as np from typing import Dict, Any, List, Optional, Tuple import time import json import logging from http_storage import LocalStorage from config import get_db_url from electron_speed import max_switch_freq, GATE_DELAY from logic_gates import LogicGate from virtual_vram import VirtualVRAM from cross_gpu_stream import CrossGPUStreamManager # Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') class GPUParallelDistributor: def __init__(self, num_gpus: int = 8): self.num_gpus = num_gpus self.storage = LocalStorage(db_url=get_db_url()) self.initialized = False self.hardware_config = None self.nvlink_topology = None # Initialize cross-GPU stream management self.stream_manager = CrossGPUStreamManager(storage_url=get_db_url()) # Performance tracking self.operation_history = {} self.gpu_load_history = {i: [] for i in range(num_gpus)} self.bandwidth_usage = {i: 0 for i in range(num_gpus)} # Scheduling parameters self.load_threshold = 0.8 # 80% load threshold self.min_chunk_size = 3024 # minimum chunk size in bytes self.max_concurrent_kernels = 128 def initialize(self, hardware_config: Dict[str, Any], nvlink_topology: Dict[str, Any]): """Initialize the distributor with hardware configuration and NVLink topology""" self.hardware_config = hardware_config self.nvlink_topology = nvlink_topology # Calculate theoretical peak performance self.peak_flops = ( hardware_config['num_chips'] * hardware_config['num_sms_per_chip'] * hardware_config['num_cores_per_sm'] * 2 # FMA operations per cycle ) * hardware_config['max_switch_freq'] # Initialize load balancing parameters # Initialize SM capacity and virtual VRAM for each GPU with unlimited memory # Get VRAM sizes from hardware config vram_sizes = hardware_config.get('per_gpu_vram_sizes', {}) default_vram_size = hardware_config.get('vram_size_gb', None) # None means unlimited for i in range(self.num_gpus): # Use per-GPU VRAM size if specified, otherwise use default vram_size = vram_sizes.get(i, default_vram_size) self.vram = {} self.sm_capacity = {} for i in range(self.num_gpus): # Initialize each GPU with unlimited VRAM self.vram[i] = VirtualVRAM(size_gb=None, storage=self.storage) # None means unlimited VRAM self.sm_capacity[i] = hardware_config['num_sms_per_chip'] # Initialize HAL database connection self.hal = self.storage.get_hal_connection() self.initialized = True def _calculate_nvlink_score(self, gpu_id: int) -> float: """Calculate NVLink connectivity score for a GPU""" total_bandwidth = 0 used_bandwidth = 0 # Query HAL for NVLink state links = self.hal.execute(""" SELECT bandwidth_tbps, state_json FROM optical_interconnects WHERE chip_a_id = ? OR chip_b_id = ? """, (gpu_id, gpu_id)).fetchall() for bandwidth_tbps, state_json in links: state = json.loads(state_json) total_bandwidth += bandwidth_tbps * 1000 # Convert to GB/s used_bandwidth += state['current_bandwidth_usage'] return 1.0 - (used_bandwidth / total_bandwidth) if total_bandwidth > 0 else 0 def _select_optimal_gpus(self, input_sizes: Dict[int, int]) -> List[int]: """Select optimal GPUs based on data locality and load""" gpu_scores = {} # Query current GPU states from HAL gpu_states = self.hal.execute(""" SELECT chip_id, state_json FROM gpu_chips WHERE chip_id < ? """, (self.num_gpus,)).fetchall() for chip_id, state_json in gpu_states: state = json.loads(state_json) # Calculate load score load_score = 1.0 - state.get('current_utilization', 0.0) # Calculate memory locality score locality_score = self._calculate_memory_locality(chip_id, input_sizes) # Calculate NVLink score nvlink_score = self._calculate_nvlink_score(chip_id) # Combined score with weights gpu_scores[chip_id] = ( 0.4 * load_score + 0.4 * locality_score + 0.2 * nvlink_score ) # Select GPUs based on scores and data size total_size = sum(input_sizes.values()) num_gpus_needed = max(1, total_size // (1024 * 1024 * 1024)) # 1 GPU per GB sorted_gpus = sorted(gpu_scores.items(), key=lambda x: x[1], reverse=True) return [gpu_id for gpu_id, _ in sorted_gpus[:num_gpus_needed]] def _register_cross_gpu_operation(self, op_type: str, distributed_ops: List[Dict[str, Any]]) -> int: """Register a cross-GPU operation in HAL database""" # Insert operation record self.hal.execute(""" INSERT INTO cross_gpu_operations ( operation_type, source_chip, target_chip, nvlink_path, start_time, state_json ) VALUES (?, ?, ?, ?, CURRENT_TIMESTAMP, ?) """, ( op_type, distributed_ops[0]['gpu_id'], distributed_ops[-1]['gpu_id'], json.dumps([op['nvlink_paths'] for op in distributed_ops]), json.dumps({ 'status': 'started', 'num_chunks': len(distributed_ops), 'completion': 0.0 }) )) return self.hal.execute("SELECT last_insert_rowid()").fetchone()[0] def _setup_memory_coherence(self, distributed_ops: List[Dict[str, Any]]): """Setup memory coherence tracking for cross-GPU operation""" for op in distributed_ops: gpu_id = op['gpu_id'] # Track input tensors for addr in op['inputs'].values(): self.hal.execute(""" INSERT OR REPLACE INTO memory_coherence ( address, chip_id, version, last_modified, dirty ) VALUES (?, ?, COALESCE((SELECT version + 1 FROM memory_coherence WHERE address = ? AND chip_id = ?), 1), CURRENT_TIMESTAMP, FALSE) """, (addr, gpu_id, addr, gpu_id)) # Track output tensors if 'output' in op: self.hal.execute(""" INSERT OR REPLACE INTO memory_coherence ( address, chip_id, version, last_modified, dirty ) VALUES (?, ?, 1, CURRENT_TIMESTAMP, TRUE) """, (op['output'], gpu_id)) self.hal.commit() def _calculate_memory_locality(self, chip_id: int, input_sizes: Dict[int, int]) -> float: """Calculate memory locality score based on data presence""" total_size = sum(input_sizes.values()) if total_size == 0: return 1.0 local_data = 0 for addr, size in input_sizes.items(): # Check if data is present in this GPU's memory result = self.hal.execute(""" SELECT COUNT(*) FROM memory_coherence WHERE address = ? AND chip_id = ? AND dirty = FALSE """, (addr, chip_id)).fetchone() if result[0] > 0: local_data += size return local_data / total_size # # Initialize NVLink bandwidth tracking # self.nvlink_bandwidth = {} # for link_id, link_info in nvlink_topology.items(): # self.nvlink_bandwidth[link_id] = { # 'capacity': link_info['bandwidth_gbps'], # 'used': 0 # } # self.initialized = True # logging.info(f"Initialized GPUParallelDistributor with {self.num_gpus} GPUs") def distribute_operation(self, operation: Dict[str, Any]) -> List[Dict[str, Any]]: """ Distribute operation across GPUs using advanced scheduling strategies: - Load balancing across GPUs - NVLink topology awareness - Memory locality optimization - Dynamic chunk sizing - Operation type specific optimizations """ if not self.initialized: raise RuntimeError("GPUParallelDistributor not initialized") op_type = operation.get("type", "") input_size = operation.get("input_size", 0) # Update GPU load history self._update_load_history() # Get optimal GPU selection based on current load and NVLink topology target_gpus = self._select_target_gpus(operation) # Calculate chunk sizes based on operation type and GPU capabilities chunk_sizes = self._calculate_chunk_sizes(operation, target_gpus) # Get distribution strategy strategy = self._get_distribution_strategy(op_type, input_size) # Calculate optimal distribution based on operation type if op_type == "matmul": distributed_ops = self._distribute_matmul(operation, target_gpus, chunk_sizes) elif op_type == "conv": distributed_ops = self._distribute_conv(operation, target_gpus, chunk_sizes) elif op_type == "tensor": distributed_ops = self._distribute_tensor(operation, target_gpus, chunk_sizes) elif op_type == "reduction": distributed_ops = self._distribute_reduction(operation, target_gpus, chunk_sizes) elif op_type == "transformer": distributed_ops = self._distribute_transformer(operation, target_gpus, chunk_sizes) else: distributed_ops = self._distribute_generic(operation, target_gpus, chunk_sizes) # Create a new stream for this distributed operation stream = self.stream_manager.create_stream() # Add performance tracking metadata and prepare operations for streaming for op in distributed_ops: # Add metadata op['metadata'] = { 'estimated_flops': self._estimate_flops(op), 'estimated_memory': self._estimate_memory(op), 'estimated_time': self._estimate_execution_time(op) } # Add the compute operation to the stream self.stream_manager.add_cross_gpu_operation(stream.stream_id, { 'type': 'compute', 'gpu_id': op['gpu_id'], 'operation': op }) # If there are dependencies on other GPUs, add transfer operations if 'dependencies' in op: for dep in op['dependencies']: self.stream_manager.add_cross_gpu_operation(stream.stream_id, { 'type': 'transfer', 'source_gpu': dep['gpu_id'], 'target_gpu': op['gpu_id'], 'size': dep['size'] }) # Add sync point if this operation needs to wait for others if op.get('sync_gpus'): self.stream_manager.add_cross_gpu_operation(stream.stream_id, { 'type': 'sync', 'gpu_ids': op['sync_gpus'] }) # Update operation history op_id = len(self.operation_history) self.operation_history[op_id] = { 'type': op_type, 'size': input_size, 'distribution': distributed_ops, 'timestamp': time.time() } return distributed_ops def _distribute_matmul(self, operation: Dict[str, Any], target_gpus: List[int], chunk_sizes: Dict[int, int]) -> List[Dict[str, Any]]: """ Distribute matrix multiplication across GPUs using advanced strategies: - 2D decomposition for large matrices - Pipeline stages for multi-GPU execution - Memory locality optimization - NVLink path optimization """ matrix_a = operation["inputs"]["A"] matrix_b = operation["inputs"]["B"] rows_a, cols_a = matrix_a.shape rows_b, cols_b = matrix_b.shape # Choose distribution strategy based on matrix sizes if rows_a >= 8192 and cols_b >= 8192: # Use 2D decomposition for large matrices return self._distribute_matmul_2d(matrix_a, matrix_b, target_gpus, chunk_sizes) else: # Use 1D decomposition with pipelining for smaller matrices return self._distribute_matmul_1d(matrix_a, matrix_b, target_gpus, chunk_sizes) def _distribute_matmul_2d(self, matrix_a: np.ndarray, matrix_b: np.ndarray, target_gpus: List[int], chunk_sizes: Dict[int, int]) -> List[Dict[str, Any]]: """Implement 2D matrix decomposition across GPUs""" rows_a, cols_a = matrix_a.shape rows_b, cols_b = matrix_b.shape # Calculate grid dimensions grid_dim = int(np.sqrt(len(target_gpus))) row_chunks = rows_a // grid_dim col_chunks = cols_b // grid_dim distributed_ops = [] for i, gpu_id in enumerate(target_gpus): grid_row = i // grid_dim grid_col = i % grid_dim # Calculate matrix chunks for this GPU row_start = grid_row * row_chunks row_end = row_start + row_chunks if grid_row < grid_dim - 1 else rows_a col_start = grid_col * col_chunks col_end = col_start + col_chunks if grid_col < grid_dim - 1 else cols_b chunk_op = { "type": "matmul_2d", "gpu_id": gpu_id, "grid_position": (grid_row, grid_col), "inputs": { "A": matrix_a[row_start:row_end, :], "B": matrix_b[:, col_start:col_end] }, "output_shape": (row_end - row_start, col_end - col_start), "communication": { "row_gpus": target_gpus[grid_row * grid_dim:(grid_row + 1) * grid_dim], "col_gpus": target_gpus[grid_col::grid_dim] } } # Add NVLink path optimization chunk_op["nvlink_paths"] = self._get_optimal_nvlink_paths(gpu_id, chunk_op["communication"]) distributed_ops.append(chunk_op) return distributed_ops def _select_target_gpus(self, operation: Dict[str, Any]) -> List[int]: """Select optimal GPUs based on load, memory, and NVLink topology""" gpu_scores = {} for gpu_id in range(self.num_gpus): # Calculate load score (lower is better) load_score = np.mean(self.gpu_load_history[gpu_id][-10:]) if self.gpu_load_history[gpu_id] else 0 # Calculate memory bandwidth score bandwidth_score = 1.0 - (self.bandwidth_usage[gpu_id] / self.hardware_config['memory_config']['bandwidth_gb_per_sec']) # Calculate NVLink connectivity score nvlink_score = self._calculate_nvlink_score(gpu_id) # Combine scores (weighted average) gpu_scores[gpu_id] = 0.4 * (1.0 - load_score) + 0.3 * bandwidth_score + 0.3 * nvlink_score # Sort GPUs by score and return top ones needed sorted_gpus = sorted(gpu_scores.items(), key=lambda x: x[1], reverse=True) num_gpus_needed = self._estimate_gpus_needed(operation) return [gpu_id for gpu_id, _ in sorted_gpus[:num_gpus_needed]] def _calculate_nvlink_score(self, gpu_id: int) -> float: """Calculate NVLink connectivity score for a GPU""" total_bandwidth = 0 used_bandwidth = 0 for link_id, link_info in self.nvlink_topology.items(): if link_info['gpu_a'] == gpu_id or link_info['gpu_b'] == gpu_id: total_bandwidth += link_info['bandwidth_gbps'] used_bandwidth += self.nvlink_bandwidth[link_id]['used'] return 1.0 - (used_bandwidth / total_bandwidth) if total_bandwidth > 0 else 0 def _estimate_gpus_needed(self, operation: Dict[str, Any]) -> int: """Estimate number of GPUs needed based on operation size and type""" op_type = operation.get("type", "") input_size = operation.get("input_size", 0) if op_type == "matmul": # For matrix multiplication, scale with matrix size matrix_a = operation["inputs"]["A"] matrix_b = operation["inputs"]["B"] flops = 2 * matrix_a.shape[0] * matrix_a.shape[1] * matrix_b.shape[1] return min(self.num_gpus, max(1, flops // (self.peak_flops // self.num_gpus))) elif op_type == "conv": # For convolution, consider input size and kernel input_tensor = operation["inputs"]["tensor"] batch_size = input_tensor.shape[0] return min(self.num_gpus, max(1, batch_size // 32)) # 32 samples per GPU else: # For generic operations, scale with input size return min(self.num_gpus, max(1, input_size // (1024 * 1024 * 1024))) # 1GB per GPU def _calculate_chunk_sizes(self, operation: Dict[str, Any], target_gpus: List[int]) -> Dict[int, int]: """Calculate optimal chunk sizes for each GPU based on their capabilities""" op_type = operation.get("type", "") total_size = operation.get("input_size", 0) chunk_sizes = {} total_compute_power = sum(self.sm_capacity[gpu_id] for gpu_id in target_gpus) for gpu_id in target_gpus: # Calculate proportion based on SM count and current load gpu_power = self.sm_capacity[gpu_id] load_factor = 1.0 - np.mean(self.gpu_load_history[gpu_id][-10:]) if self.gpu_load_history[gpu_id] else 1.0 proportion = (gpu_power * load_factor) / total_compute_power chunk_sizes[gpu_id] = max(self.min_chunk_size, int(total_size * proportion)) return chunk_sizes def _update_load_history(self): """Update GPU load history with current utilization""" for gpu_id in range(self.num_gpus): current_load = len([op for op in self.operation_history.values() if any(sub_op['gpu_id'] == gpu_id for sub_op in op['distribution'])]) self.gpu_load_history[gpu_id].append(current_load / self.max_concurrent_kernels) # Keep history length manageable if len(self.gpu_load_history[gpu_id]) > 100: self.gpu_load_history[gpu_id] = self.gpu_load_history[gpu_id][-100:] def _distribute_conv(self, operation: Dict[str, Any], target_gpus: List[int], chunk_sizes: Dict[int, int]) -> List[Dict[str, Any]]: """ Distribute convolution operation across GPUs using database storage for operation tracking and tensor data management. """ input_tensor = operation["inputs"]["tensor"] kernel = operation["inputs"]["kernel"] batch_size = input_tensor.shape[0] # Store the full input tensor and kernel in database input_key = f"conv_input_{time.time_ns()}" kernel_key = f"conv_kernel_{time.time_ns()}" # Store tensors in database with compression self.storage.store(input_key, { 'data': input_tensor.tobytes(), 'shape': input_tensor.shape, 'dtype': str(input_tensor.dtype) }, compress=True) self.storage.store(kernel_key, { 'data': kernel.tobytes(), 'shape': kernel.shape, 'dtype': str(kernel.dtype) }, compress=True) distributed_ops = [] op_tracking_key = f"conv_op_{time.time_ns()}" try: # Calculate optimal chunk distribution chunks_per_gpu = self._calculate_optimal_chunks(batch_size, len(target_gpus)) for i, gpu_id in enumerate(target_gpus): start_batch = sum(chunks_per_gpu[:i]) end_batch = start_batch + chunks_per_gpu[i] # Create chunk operation record in database chunk_key = f"{op_tracking_key}_chunk_{gpu_id}" chunk_op = { "type": "conv", "gpu_id": gpu_id, "input_key": input_key, "kernel_key": kernel_key, "batch_range": (start_batch, end_batch), "memory_config": { "cache_mode": "l1_cached", "prefetch_enabled": True, "chunk_size": chunk_sizes[gpu_id] }, "nvlink_paths": self._get_optimal_nvlink_paths(gpu_id, { "input_size": (end_batch - start_batch) * np.prod(input_tensor.shape[1:]), "kernel_size": np.prod(kernel.shape) }) } # Store chunk operation configuration self.storage.store(chunk_key, chunk_op) # Create operation descriptor with database references distributed_ops.append({ "type": "conv", "gpu_id": gpu_id, "op_key": chunk_key, "input_ref": { "key": input_key, "range": (start_batch, end_batch) }, "kernel_ref": { "key": kernel_key } }) # Store operation tracking metadata self.storage.store(op_tracking_key, { "type": "conv_distribution", "num_gpus": len(target_gpus), "chunks": chunks_per_gpu, "input_key": input_key, "kernel_key": kernel_key, "chunk_keys": [f"{op_tracking_key}_chunk_{gpu_id}" for gpu_id in target_gpus] }) return distributed_ops except Exception as e: # Cleanup on failure self.storage.delete(input_key) self.storage.delete(kernel_key) self.storage.delete(op_tracking_key) for gpu_id in target_gpus: self.storage.delete(f"{op_tracking_key}_chunk_{gpu_id}") raise e def _calculate_optimal_chunks(self, total_size: int, num_gpus: int) -> List[int]: """Calculate optimal chunk sizes based on GPU capabilities and current load""" chunks = [] remaining = total_size # Get recent GPU loads gpu_loads = { gpu_id: np.mean(self.gpu_load_history[gpu_id][-10:]) if self.gpu_load_history[gpu_id] else 0 for gpu_id in range(num_gpus) } # Normalize load factors (inverse of load, so higher means more available) total_availability = sum(1.0 - load for load in gpu_loads.values()) if total_availability == 0: total_availability = num_gpus # Equal distribution if all fully loaded for i in range(num_gpus): if i == num_gpus - 1: chunks.append(remaining) else: # Calculate chunk size based on GPU availability load_factor = 1.0 - gpu_loads[i] chunk_size = int((load_factor / total_availability) * total_size) chunk_size = max(1, min(chunk_size, remaining)) # Ensure valid size chunks.append(chunk_size) remaining -= chunk_size return chunks def distribute_operation(self, input_tensors_memory_size: Dict[int, int]) -> List[Dict[str, Any]]: """ Distribute operations across GPUs based on input tensor sizes and GPU states Args: input_tensors_memory_size: Dictionary mapping tensor addresses to their sizes in bytes Returns: List of distributed operations with their configurations # chunk_sizes: Dict[int, int]) -> List[Dict[str, Any]]: # """ # Distribute generic operation across GPUs with advanced features: # - Dynamic load balancing # - Pipeline staging # - Memory access optimization # """ # Get operation parameters tensor_sizes = input_tensors_memory_size gpus = self._select_optimal_gpus(tensor_sizes) if not gpus: logging.warning("No suitable GPUs found for operation") return None # Calculate chunk size per GPU based on memory and compute capacity chunk_sizes = {} total_size = sum(tensor_sizes.values()) for gpu_id in gpus: gpu_state = json.loads(self.hal.execute(""" SELECT state_json FROM gpu_chips WHERE chip_id = ? """, (gpu_id,)).fetchone()[0]) free_memory = gpu_state.get('free_memory_bytes', 0) chunk_sizes[gpu_id] = min(free_memory // 2, total_size // len(gpus)) # Calculate pipeline stages num_stages = min(len(gpus), max(1, total_size // self.min_chunk_size)) stage_size = total_size // num_stages distributed_ops = [] for stage, gpu_id in enumerate(gpus[:num_stages]): start_idx = stage * stage_size end_idx = start_idx + stage_size if stage < num_stages - 1 else total_size # Create pipeline stage operation stage_op = { "type": "distributed_tensor", "gpu_id": gpu_id, "stage": stage, "num_stages": num_stages, "range": (start_idx, end_idx), "chunk_size": chunk_sizes[gpu_id], "pipeline_config": { "stage_id": stage, "total_stages": num_stages, "next_gpu": gpus[(stage + 1) % num_stages] if stage < num_stages - 1 else None, "prev_gpu": gpus[stage - 1] if stage > 0 else None } } # Add memory access pattern optimization stage_op["memory_access"] = self._optimize_memory_access(stage_op) # Add synchronization points stage_op["sync_points"] = self._generate_sync_points(stage_op) distributed_ops.append(stage_op) return distributed_ops def _calculate_optimal_pipeline_stages(self, operation: Dict[str, Any]) -> int: """Calculate optimal number of pipeline stages based on operation characteristics""" # Consider memory bandwidth, compute intensity, and data dependencies op_type = operation.get("type", "") input_size = operation.get("input_size", 0) if op_type in ["reduction", "scan"]: # Operations with strong data dependencies benefit from fewer stages return min(3, self.num_gpus) elif op_type in ["map", "filter"]: # Embarrassingly parallel operations can use more stages return min(8, self.num_gpus) else: # Default to moderate pipeline depth return min(4, self.num_gpus) def _optimize_memory_access(self, stage_op: Dict[str, Any]) -> Dict[str, Any]: """Optimize memory access patterns for the operation""" return { "access_pattern": "sequential" if stage_op["type"] in ["reduction", "scan"] else "strided", "prefetch_distance": 2 if stage_op["type"] in ["map", "filter"] else 1, "cache_hint": "temporal" if stage_op["type"] in ["matmul", "conv"] else "spatial" } def _generate_sync_points(self, stage_op: Dict[str, Any]) -> List[Dict[str, Any]]: """Generate synchronization points for pipeline stages""" sync_points = [] if stage_op["pipeline_config"]["prev_gpu"] is not None: sync_points.append({ "type": "wait", "gpu_id": stage_op["pipeline_config"]["prev_gpu"], "stage": stage_op["stage"] - 1 }) if stage_op["pipeline_config"]["next_gpu"] is not None: sync_points.append({ "type": "signal", "gpu_id": stage_op["pipeline_config"]["next_gpu"], "stage": stage_op["stage"] }) return sync_points async def distribute_cuda_ops(self, tensor_data: Dict[str, Any], workload_per_core: float, total_cores: int) -> Dict[str, Any]: """Distribute operations optimized for CUDA cores.""" try: data = tensor_data['data'] operation = tensor_data.get('operation', 'generic') # Split data across available CUDA cores chunk_size = int(len(data) / total_cores) chunks = [] for i in range(0, total_cores): start_idx = i * chunk_size end_idx = start_idx + chunk_size if i < total_cores - 1 else len(data) chunk_data = data[start_idx:end_idx] chunk_op = { "type": "cuda", "operation": operation, "data": chunk_data, "core_id": i } chunks.append(chunk_op) # Process chunks in parallel using CUDA cores results = await self._process_cuda_chunks(chunks) # Combine results combined_data = np.concatenate([r['data'] for r in results]) return { 'status': 'success', 'operation': operation, 'data': combined_data } except Exception as e: return { 'status': 'error', 'operation': tensor_data.get('operation', 'unknown'), 'message': str(e), 'data': [] } async def distribute_tensor_ops(self, tensor_data: Dict[str, Any], workload_per_core: float, total_cores: int) -> Dict[str, Any]: """Distribute operations optimized for tensor cores.""" try: data = tensor_data['data'] operation = tensor_data.get('operation', 'matmul') # Split data into chunks optimal for tensor core processing chunk_size = int(len(data) / total_cores) chunks = [] for i in range(0, total_cores): start_idx = i * chunk_size end_idx = start_idx + chunk_size if i < total_cores - 1 else len(data) chunk_data = data[start_idx:end_idx] chunk_op = { "type": "tensor", "operation": operation, "data": chunk_data, "core_id": i } chunks.append(chunk_op) # Process chunks using tensor cores (optimized for matrix/tensor operations) results = await self._process_tensor_chunks(chunks) # Combine results combined_data = np.concatenate([r['data'] for r in results]) return { 'status': 'success', 'operation': operation, 'data': combined_data } except Exception as e: return { 'status': 'error', 'operation': tensor_data.get('operation', 'unknown'), 'message': str(e), 'data': [] } async def _process_cuda_chunks(self, chunks: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """Process data chunks using CUDA cores.""" results = [] for chunk in chunks: # Process based on operation type if chunk['operation'] == 'elemwise': result = self._process_elemwise_cuda(chunk['data']) elif chunk['operation'] == 'reduction': result = self._process_reduction_cuda(chunk['data']) else: result = self._process_generic_cuda(chunk['data']) results.append({'data': result, 'core_id': chunk['core_id']}) return results async def _process_tensor_chunks(self, chunks: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """Process data chunks using tensor cores.""" results = [] for chunk in chunks: # Process based on operation type if chunk['operation'] == 'matmul': result = self._process_matmul_tensor(chunk['data']) elif chunk['operation'] == 'conv2d': result = self._process_conv2d_tensor(chunk['data']) else: result = self._process_generic_tensor(chunk['data']) results.append({'data': result, 'core_id': chunk['core_id']}) return results def _process_elemwise_cuda(self, data: np.ndarray) -> np.ndarray: """Process element-wise operations using CUDA cores.""" # Simulate CUDA core processing for element-wise operations return data * 2 # Example operation def _process_reduction_cuda(self, data: np.ndarray) -> np.ndarray: """Process reduction operations using CUDA cores.""" # Simulate CUDA core processing for reduction operations return np.sum(data, axis=0) def _process_generic_cuda(self, data: np.ndarray) -> np.ndarray: """Process generic operations using CUDA cores.""" # Simulate general-purpose CUDA processing return data + 1 # Example operation def _process_matmul_tensor(self, data: np.ndarray) -> np.ndarray: """Process matrix multiplication using tensor cores.""" # Simulate tensor core processing for matrix multiplication if len(data.shape) < 2: data = data.reshape((-1, 1)) return np.matmul(data, data.T) def _process_conv2d_tensor(self, data: np.ndarray) -> np.ndarray: """Process 2D convolution using tensor cores.""" # Simulate tensor core processing for 2D convolution kernel = np.ones((3, 3)) / 9 # Example 3x3 averaging kernel return np.apply_along_axis(lambda x: np.convolve(x, kernel.flatten(), mode='same'), axis=0, arr=data) def _process_generic_tensor(self, data: np.ndarray) -> np.ndarray: """Process generic operations using tensor cores.""" # Simulate general tensor core processing if len(data.shape) < 2: data = data.reshape((-1, 1)) return np.matmul(data, np.ones_like(data)) # Example operation