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"""
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 |