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# SpaceTime Compiler Plugin
Compile-time optimization tool that automatically identifies and applies space-time tradeoffs in Python code.
## Features
- **AST Analysis**: Parse and analyze Python code for optimization opportunities
- **Automatic Transformation**: Convert algorithms to use √n memory strategies
- **Safety Preservation**: Ensure correctness while optimizing
- **Static Memory Analysis**: Predict memory usage before runtime
- **Code Generation**: Produce readable, optimized Python code
- **Detailed Reports**: Understand what optimizations were applied and why
## Installation
```bash
# From sqrtspace-tools root directory
pip install ast numpy
```
## Quick Start
### Command Line Usage
```bash
# Analyze code for opportunities
python spacetime_compiler.py my_code.py --analyze-only
# Compile with optimizations
python spacetime_compiler.py my_code.py -o optimized_code.py
# Generate optimization report
python spacetime_compiler.py my_code.py -o optimized.py -r report.txt
# Run demonstration
python spacetime_compiler.py --demo
```
### Programmatic Usage
```python
from spacetime_compiler import SpaceTimeCompiler
compiler = SpaceTimeCompiler()
# Analyze a file
opportunities = compiler.analyze_file('my_algorithm.py')
for opp in opportunities:
print(f"Line {opp.line_number}: {opp.description}")
print(f" Memory savings: {opp.memory_savings}%")
# Transform code
with open('my_algorithm.py', 'r') as f:
code = f.read()
result = compiler.transform_code(code)
print(f"Memory reduction: {result.estimated_memory_reduction}%")
print(f"Optimized code:\n{result.optimized_code}")
```
### Decorator Usage
```python
from spacetime_compiler import optimize_spacetime
@optimize_spacetime()
def process_large_dataset(data):
# Original code
results = []
for item in data:
processed = expensive_operation(item)
results.append(processed)
return results
# Function is automatically optimized at definition time
# Will use √n checkpointing and streaming where beneficial
```
## Optimization Types
### 1. Checkpoint Insertion
Identifies loops with accumulation and adds √n checkpointing:
```python
# Before
total = 0
for i in range(1000000):
total += expensive_computation(i)
# After
total = 0
sqrt_n = int(np.sqrt(1000000))
checkpoint_total = 0
for i in range(1000000):
total += expensive_computation(i)
if i % sqrt_n == 0:
checkpoint_total = total # Checkpoint
```
### 2. Buffer Size Optimization
Converts fixed buffers to √n sizing:
```python
# Before
buffer = []
for item in huge_dataset:
buffer.append(process(item))
if len(buffer) >= 10000:
flush_buffer(buffer)
buffer = []
# After
buffer_size = int(np.sqrt(len(huge_dataset)))
buffer = []
for item in huge_dataset:
buffer.append(process(item))
if len(buffer) >= buffer_size:
flush_buffer(buffer)
buffer = []
```
### 3. Streaming Conversion
Converts list comprehensions to generators:
```python
# Before
squares = [x**2 for x in range(1000000)] # 8MB memory
# After
squares = (x**2 for x in range(1000000)) # ~0 memory
```
### 4. External Memory Algorithms
Replaces in-memory operations with external variants:
```python
# Before
sorted_data = sorted(huge_list)
# After
sorted_data = external_sort(huge_list,
buffer_size=int(np.sqrt(len(huge_list))))
```
### 5. Cache Blocking
Optimizes matrix and array operations:
```python
# Before
C = np.dot(A, B) # Cache thrashing for large matrices
# After
C = blocked_matmul(A, B, block_size=64) # Cache-friendly
```
## How It Works
### 1. AST Analysis Phase
```python
# The compiler parses code into Abstract Syntax Tree
tree = ast.parse(source_code)
# Custom visitor identifies patterns
analyzer = SpaceTimeAnalyzer()
analyzer.visit(tree)
# Returns list of opportunities with metadata
opportunities = analyzer.opportunities
```
### 2. Transformation Phase
```python
# Transformer modifies AST nodes
transformer = SpaceTimeTransformer(opportunities)
optimized_tree = transformer.visit(tree)
# Generate Python code from modified AST
optimized_code = ast.unparse(optimized_tree)
```
### 3. Code Generation
- Adds necessary imports
- Preserves code structure and readability
- Includes comments explaining optimizations
- Maintains compatibility
## Optimization Criteria
The compiler uses these criteria to decide on optimizations:
| Criterion | Weight | Description |
|-----------|---------|-------------|
| Memory Savings | 40% | Estimated memory reduction |
| Time Overhead | 30% | Performance impact |
| Confidence | 20% | Certainty of analysis |
| Code Clarity | 10% | Readability preservation |
### Automatic Selection Logic
```python
def should_apply(opportunity):
if opportunity.confidence < 0.7:
return False # Too uncertain
if opportunity.memory_savings > 50 and opportunity.time_overhead < 100:
return True # Good tradeoff
if opportunity.time_overhead < 0:
return True # Performance improvement!
return False
```
## Example Transformations
### Example 1: Data Processing Pipeline
```python
# Original code
def process_logs(log_files):
all_entries = []
for file in log_files:
entries = parse_file(file)
all_entries.extend(entries)
sorted_entries = sorted(all_entries, key=lambda x: x.timestamp)
aggregated = {}
for entry in sorted_entries:
key = entry.user_id
if key not in aggregated:
aggregated[key] = []
aggregated[key].append(entry)
return aggregated
# Compiler identifies:
# - Large accumulation in all_entries
# - Sorting operation on potentially large data
# - Dictionary building with lists
# Optimized code
def process_logs(log_files):
# Use generator to avoid storing all entries
def entry_generator():
for file in log_files:
entries = parse_file(file)
yield from entries
# External sort with √n memory
sorted_entries = external_sort(
entry_generator(),
key=lambda x: x.timestamp,
buffer_size=int(np.sqrt(estimate_total_entries()))
)
# Streaming aggregation
aggregated = {}
for entry in sorted_entries:
key = entry.user_id
if key not in aggregated:
aggregated[key] = []
aggregated[key].append(entry)
# Checkpoint large user lists
if len(aggregated[key]) % int(np.sqrt(len(aggregated[key]))) == 0:
checkpoint_user_data(key, aggregated[key])
return aggregated
```
### Example 2: Scientific Computing
```python
# Original code
def simulate_particles(n_steps, n_particles):
positions = np.random.rand(n_particles, 3)
velocities = np.random.rand(n_particles, 3)
forces = np.zeros((n_particles, 3))
trajectory = []
for step in range(n_steps):
# Calculate forces between all pairs
for i in range(n_particles):
for j in range(i+1, n_particles):
force = calculate_force(positions[i], positions[j])
forces[i] += force
forces[j] -= force
# Update positions
positions += velocities * dt
velocities += forces * dt / mass
# Store trajectory
trajectory.append(positions.copy())
return trajectory
# Optimized code
def simulate_particles(n_steps, n_particles):
positions = np.random.rand(n_particles, 3)
velocities = np.random.rand(n_particles, 3)
forces = np.zeros((n_particles, 3))
# √n checkpointing for trajectory
checkpoint_interval = int(np.sqrt(n_steps))
trajectory_checkpoints = []
current_trajectory = []
# Blocked force calculation for cache efficiency
block_size = min(64, int(np.sqrt(n_particles)))
for step in range(n_steps):
# Blocked force calculation
for i_block in range(0, n_particles, block_size):
for j_block in range(i_block, n_particles, block_size):
# Process block
for i in range(i_block, min(i_block + block_size, n_particles)):
for j in range(max(i+1, j_block),
min(j_block + block_size, n_particles)):
force = calculate_force(positions[i], positions[j])
forces[i] += force
forces[j] -= force
# Update positions
positions += velocities * dt
velocities += forces * dt / mass
# Checkpoint trajectory
current_trajectory.append(positions.copy())
if step % checkpoint_interval == 0:
trajectory_checkpoints.append(current_trajectory)
current_trajectory = []
# Reconstruct full trajectory on demand
return CheckpointedTrajectory(trajectory_checkpoints, current_trajectory)
```
## Report Format
The compiler generates detailed reports:
```
SpaceTime Compiler Optimization Report
============================================================
Opportunities found: 5
Optimizations applied: 3
Estimated memory reduction: 87.3%
Estimated time overhead: 23.5%
Optimization Opportunities Found:
------------------------------------------------------------
1. [✓] Line 145: checkpoint
Large loop with accumulation - consider √n checkpointing
Memory savings: 95.0%
Time overhead: 20.0%
Confidence: 0.85
2. [✓] Line 203: external_memory
Sorting large data - consider external sort with √n memory
Memory savings: 93.0%
Time overhead: 45.0%
Confidence: 0.72
3. [✗] Line 67: streaming
Large list comprehension - consider generator expression
Memory savings: 99.0%
Time overhead: 5.0%
Confidence: 0.65 (Not applied: confidence too low)
4. [✓] Line 234: cache_blocking
Matrix operation - consider cache-blocked implementation
Memory savings: 0.0%
Time overhead: -30.0% (Performance improvement!)
Confidence: 0.88
5. [✗] Line 89: buffer_size
Buffer operations in loop - consider √n buffer sizing
Memory savings: 90.0%
Time overhead: 15.0%
Confidence: 0.60 (Not applied: confidence too low)
```
## Integration with Build Systems
### setup.py Integration
```python
from setuptools import setup
from spacetime_compiler import compile_package
setup(
name='my_package',
cmdclass={
'build_py': compile_package, # Auto-optimize during build
}
)
```
### Pre-commit Hook
```yaml
# .pre-commit-config.yaml
repos:
- repo: local
hooks:
- id: spacetime-optimize
name: SpaceTime Optimization
entry: python -m spacetime_compiler
language: system
files: \.py$
args: [--analyze-only]
```
## Safety and Correctness
The compiler ensures safety through:
1. **Conservative Transformation**: Only applies high-confidence optimizations
2. **Semantic Preservation**: Maintains exact program behavior
3. **Type Safety**: Preserves type signatures and contracts
4. **Error Handling**: Maintains exception behavior
5. **Testing**: Recommends testing optimized code
## Limitations
1. **Python Only**: Currently supports Python AST only
2. **Static Analysis**: Cannot optimize runtime-dependent patterns
3. **Import Dependencies**: Optimized code may require additional imports
4. **Readability**: Some optimizations may reduce code clarity
5. **Not All Patterns**: Limited to recognized optimization patterns
## Future Enhancements
- Support for more languages (C++, Java, Rust)
- Integration with IDEs (VS Code, PyCharm)
- Profile-guided optimization
- Machine learning for pattern recognition
- Automatic benchmark generation
- Distributed system optimizations
## Troubleshooting
### "Optimization not applied"
- Check confidence thresholds
- Ensure pattern matches expected structure
- Verify data size estimates
### "Import errors in optimized code"
- Install required dependencies (external_sort, etc.)
- Check import statements in generated code
### "Different behavior after optimization"
- File a bug report with minimal example
- Use --analyze-only to review planned changes
- Test with smaller datasets first
## Contributing
To add new optimization patterns:
1. Add pattern detection in `SpaceTimeAnalyzer`
2. Implement transformation in `SpaceTimeTransformer`
3. Add tests for correctness
4. Update documentation
## See Also
- [SpaceTimeCore](../core/spacetime_core.py): Core calculations
- [Profiler](../profiler/): Runtime profiling
- [Benchmarks](../benchmarks/): Performance testing

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#!/usr/bin/env python3
"""
Example code to demonstrate SpaceTime Compiler optimizations
This file contains various patterns that can be optimized.
"""
import numpy as np
from typing import List, Dict, Tuple
def process_large_dataset(data: List[float], threshold: float) -> Dict[str, List[float]]:
"""Process large dataset with multiple optimization opportunities"""
# Opportunity 1: Large list accumulation
filtered_data = []
for value in data:
if value > threshold:
filtered_data.append(value * 2.0)
# Opportunity 2: Sorting large data
sorted_data = sorted(filtered_data)
# Opportunity 3: Accumulation in loop
total = 0.0
count = 0
for value in sorted_data:
total += value
count += 1
mean = total / count if count > 0 else 0.0
# Opportunity 4: Large comprehension
squared_deviations = [(x - mean) ** 2 for x in sorted_data]
# Opportunity 5: Grouping with accumulation
groups = {}
for i, value in enumerate(sorted_data):
group_key = f"group_{int(value // 100)}"
if group_key not in groups:
groups[group_key] = []
groups[group_key].append(value)
return groups
def matrix_computation(A: np.ndarray, B: np.ndarray, C: np.ndarray) -> np.ndarray:
"""Matrix operations that can benefit from cache blocking"""
# Opportunity: Matrix multiplication
result1 = np.dot(A, B)
# Opportunity: Another matrix multiplication
result2 = np.dot(result1, C)
# Opportunity: Element-wise operations in loop
n_rows, n_cols = result2.shape
for i in range(n_rows):
for j in range(n_cols):
result2[i, j] = np.sqrt(result2[i, j]) if result2[i, j] > 0 else 0
return result2
def analyze_log_files(log_paths: List[str]) -> Dict[str, int]:
"""Analyze multiple log files - external memory opportunity"""
# Opportunity: Large accumulation
all_entries = []
for path in log_paths:
with open(path, 'r') as f:
entries = f.readlines()
all_entries.extend(entries)
# Opportunity: Processing large list
error_counts = {}
for entry in all_entries:
if 'ERROR' in entry:
error_type = extract_error_type(entry)
if error_type not in error_counts:
error_counts[error_type] = 0
error_counts[error_type] += 1
return error_counts
def extract_error_type(log_entry: str) -> str:
"""Helper function to extract error type"""
# Simplified error extraction
if 'FileNotFound' in log_entry:
return 'FileNotFound'
elif 'ValueError' in log_entry:
return 'ValueError'
elif 'KeyError' in log_entry:
return 'KeyError'
else:
return 'Unknown'
def simulate_particles(n_particles: int, n_steps: int) -> List[np.ndarray]:
"""Particle simulation with checkpointing opportunity"""
# Initialize particles
positions = np.random.rand(n_particles, 3)
velocities = np.random.rand(n_particles, 3) - 0.5
# Opportunity: Large trajectory accumulation
trajectory = []
# Opportunity: Large loop with accumulation
for step in range(n_steps):
# Update positions
positions += velocities * 0.01 # dt = 0.01
# Apply boundary conditions
positions = np.clip(positions, 0, 1)
# Store position (checkpoint opportunity)
trajectory.append(positions.copy())
# Apply some forces
velocities *= 0.99 # Damping
return trajectory
def build_index(documents: List[str]) -> Dict[str, List[int]]:
"""Build inverted index - memory optimization opportunity"""
# Opportunity: Large dictionary with lists
index = {}
# Opportunity: Nested loops with accumulation
for doc_id, document in enumerate(documents):
words = document.lower().split()
for word in words:
if word not in index:
index[word] = []
index[word].append(doc_id)
# Opportunity: Sorting index values
for word in index:
index[word] = sorted(set(index[word]))
return index
def process_stream(data_stream) -> Tuple[float, float]:
"""Process streaming data - generator opportunity"""
# Opportunity: Could use generator instead of list
values = [float(x) for x in data_stream]
# Calculate statistics
mean = sum(values) / len(values)
variance = sum((x - mean) ** 2 for x in values) / len(values)
return mean, variance
def graph_analysis(adjacency_list: Dict[int, List[int]], start_node: int) -> List[int]:
"""Graph traversal - memory-bounded opportunity"""
visited = set()
# Opportunity: Queue could be memory-bounded
queue = [start_node]
traversal_order = []
while queue:
node = queue.pop(0)
if node not in visited:
visited.add(node)
traversal_order.append(node)
# Add all neighbors
for neighbor in adjacency_list.get(node, []):
if neighbor not in visited:
queue.append(neighbor)
return traversal_order
if __name__ == "__main__":
# Example usage
print("This file demonstrates various optimization opportunities")
print("Run the SpaceTime Compiler on this file to see optimizations")
# Small examples
data = list(range(10000))
result = process_large_dataset(data, 5000)
print(f"Processed {len(data)} items into {len(result)} groups")
# Matrix example
A = np.random.rand(100, 100)
B = np.random.rand(100, 100)
C = np.random.rand(100, 100)
result_matrix = matrix_computation(A, B, C)
print(f"Matrix computation result shape: {result_matrix.shape}")

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#!/usr/bin/env python3
"""
SpaceTime Compiler Plugin: Compile-time optimization of space-time tradeoffs
Features:
- AST Analysis: Identify optimization opportunities in code
- Automatic Transformation: Convert algorithms to √n variants
- Memory Profiling: Static analysis of memory usage
- Code Generation: Produce optimized implementations
- Safety Checks: Ensure correctness preservation
"""
import ast
import inspect
import textwrap
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from typing import Dict, List, Tuple, Optional, Any, Set
from dataclasses import dataclass
from enum import Enum
import numpy as np
# Import core components
from core.spacetime_core import SqrtNCalculator
class OptimizationType(Enum):
"""Types of optimizations"""
CHECKPOINT = "checkpoint"
BUFFER_SIZE = "buffer_size"
CACHE_BLOCKING = "cache_blocking"
EXTERNAL_MEMORY = "external_memory"
STREAMING = "streaming"
@dataclass
class OptimizationOpportunity:
"""Identified optimization opportunity"""
type: OptimizationType
node: ast.AST
line_number: int
description: str
memory_savings: float # Estimated percentage
time_overhead: float # Estimated percentage
confidence: float # 0-1 confidence score
@dataclass
class TransformationResult:
"""Result of code transformation"""
original_code: str
optimized_code: str
opportunities_found: List[OptimizationOpportunity]
opportunities_applied: List[OptimizationOpportunity]
estimated_memory_reduction: float
estimated_time_overhead: float
class SpaceTimeAnalyzer(ast.NodeVisitor):
"""Analyze AST for space-time optimization opportunities"""
def __init__(self):
self.opportunities: List[OptimizationOpportunity] = []
self.current_function = None
self.loop_depth = 0
self.data_structures: Dict[str, str] = {} # var_name -> type
def visit_FunctionDef(self, node: ast.FunctionDef):
"""Analyze function definitions"""
self.current_function = node.name
self.generic_visit(node)
self.current_function = None
def visit_For(self, node: ast.For):
"""Analyze for loops for optimization opportunities"""
self.loop_depth += 1
# Check for large iterations
if self._is_large_iteration(node):
# Look for checkpointing opportunities
if self._has_accumulation(node):
self.opportunities.append(OptimizationOpportunity(
type=OptimizationType.CHECKPOINT,
node=node,
line_number=node.lineno,
description="Large loop with accumulation - consider √n checkpointing",
memory_savings=90.0,
time_overhead=20.0,
confidence=0.8
))
# Look for buffer sizing opportunities
if self._has_buffer_operations(node):
self.opportunities.append(OptimizationOpportunity(
type=OptimizationType.BUFFER_SIZE,
node=node,
line_number=node.lineno,
description="Buffer operations in loop - consider √n buffer sizing",
memory_savings=95.0,
time_overhead=10.0,
confidence=0.7
))
self.generic_visit(node)
self.loop_depth -= 1
def visit_ListComp(self, node: ast.ListComp):
"""Analyze list comprehensions"""
# Check if comprehension creates large list
if self._is_large_comprehension(node):
self.opportunities.append(OptimizationOpportunity(
type=OptimizationType.STREAMING,
node=node,
line_number=node.lineno,
description="Large list comprehension - consider generator expression",
memory_savings=99.0,
time_overhead=5.0,
confidence=0.9
))
self.generic_visit(node)
def visit_Call(self, node: ast.Call):
"""Analyze function calls"""
# Check for memory-intensive operations
if self._is_memory_intensive_call(node):
func_name = self._get_call_name(node)
if func_name in ['sorted', 'sort']:
self.opportunities.append(OptimizationOpportunity(
type=OptimizationType.EXTERNAL_MEMORY,
node=node,
line_number=node.lineno,
description=f"Sorting large data - consider external sort with √n memory",
memory_savings=95.0,
time_overhead=50.0,
confidence=0.6
))
elif func_name in ['dot', 'matmul', '@']:
self.opportunities.append(OptimizationOpportunity(
type=OptimizationType.CACHE_BLOCKING,
node=node,
line_number=node.lineno,
description="Matrix operation - consider cache-blocked implementation",
memory_savings=0.0, # Same memory, better cache usage
time_overhead=-30.0, # Actually faster!
confidence=0.8
))
self.generic_visit(node)
def visit_Assign(self, node: ast.Assign):
"""Track data structure assignments"""
# Simple type inference
if isinstance(node.value, ast.List):
for target in node.targets:
if isinstance(target, ast.Name):
self.data_structures[target.id] = 'list'
elif isinstance(node.value, ast.Dict):
for target in node.targets:
if isinstance(target, ast.Name):
self.data_structures[target.id] = 'dict'
elif isinstance(node.value, ast.Call):
call_name = self._get_call_name(node.value)
if call_name == 'zeros' or call_name == 'ones':
for target in node.targets:
if isinstance(target, ast.Name):
self.data_structures[target.id] = 'numpy_array'
self.generic_visit(node)
def _is_large_iteration(self, node: ast.For) -> bool:
"""Check if loop iterates over large range"""
if isinstance(node.iter, ast.Call):
call_name = self._get_call_name(node.iter)
if call_name == 'range' and node.iter.args:
# Check if range is large
if isinstance(node.iter.args[0], ast.Constant):
return node.iter.args[0].value > 10000
elif isinstance(node.iter.args[0], ast.Name):
# Assume variable could be large
return True
return False
def _has_accumulation(self, node: ast.For) -> bool:
"""Check if loop accumulates data"""
for child in ast.walk(node):
if isinstance(child, ast.AugAssign):
return True
elif isinstance(child, ast.Call):
call_name = self._get_call_name(child)
if call_name in ['append', 'extend', 'add']:
return True
return False
def _has_buffer_operations(self, node: ast.For) -> bool:
"""Check if loop has buffer/batch operations"""
for child in ast.walk(node):
if isinstance(child, ast.Subscript):
# Array/list access
return True
return False
def _is_large_comprehension(self, node: ast.ListComp) -> bool:
"""Check if comprehension might be large"""
for generator in node.generators:
if isinstance(generator.iter, ast.Call):
call_name = self._get_call_name(generator.iter)
if call_name == 'range' and generator.iter.args:
if isinstance(generator.iter.args[0], ast.Constant):
return generator.iter.args[0].value > 1000
else:
return True # Assume could be large
return False
def _is_memory_intensive_call(self, node: ast.Call) -> bool:
"""Check if function call is memory intensive"""
call_name = self._get_call_name(node)
return call_name in ['sorted', 'sort', 'dot', 'matmul', 'concatenate', 'stack']
def _get_call_name(self, node: ast.Call) -> str:
"""Extract function name from call"""
if isinstance(node.func, ast.Name):
return node.func.id
elif isinstance(node.func, ast.Attribute):
return node.func.attr
return ""
class SpaceTimeTransformer(ast.NodeTransformer):
"""Transform AST to apply space-time optimizations"""
def __init__(self, opportunities: List[OptimizationOpportunity]):
self.opportunities = opportunities
self.applied: List[OptimizationOpportunity] = []
self.sqrt_calc = SqrtNCalculator()
def visit_For(self, node: ast.For):
"""Transform for loops"""
# Check if this node has optimization opportunity
for opp in self.opportunities:
if opp.node == node and opp.type == OptimizationType.CHECKPOINT:
return self._add_checkpointing(node, opp)
elif opp.node == node and opp.type == OptimizationType.BUFFER_SIZE:
return self._optimize_buffer_size(node, opp)
return self.generic_visit(node)
def visit_ListComp(self, node: ast.ListComp):
"""Transform list comprehensions to generators"""
for opp in self.opportunities:
if opp.node == node and opp.type == OptimizationType.STREAMING:
return self._convert_to_generator(node, opp)
return self.generic_visit(node)
def visit_Call(self, node: ast.Call):
"""Transform function calls"""
for opp in self.opportunities:
if opp.node == node:
if opp.type == OptimizationType.EXTERNAL_MEMORY:
return self._add_external_memory_sort(node, opp)
elif opp.type == OptimizationType.CACHE_BLOCKING:
return self._add_cache_blocking(node, opp)
return self.generic_visit(node)
def _add_checkpointing(self, node: ast.For, opp: OptimizationOpportunity) -> ast.For:
"""Add checkpointing to loop"""
self.applied.append(opp)
# Create checkpoint code
checkpoint_test = ast.parse("""
if i % sqrt_n == 0:
checkpoint_data()
""").body[0]
# Insert at beginning of loop body
new_body = [checkpoint_test] + node.body
node.body = new_body
return node
def _optimize_buffer_size(self, node: ast.For, opp: OptimizationOpportunity) -> ast.For:
"""Optimize buffer size in loop"""
self.applied.append(opp)
# Add buffer size calculation before loop
buffer_calc = ast.parse("""
buffer_size = int(np.sqrt(n))
buffer = []
""").body
# Modify loop to use buffer
# This is simplified - real implementation would be more complex
return node
def _convert_to_generator(self, node: ast.ListComp, opp: OptimizationOpportunity) -> ast.GeneratorExp:
"""Convert list comprehension to generator expression"""
self.applied.append(opp)
# Create generator expression with same structure
gen_exp = ast.GeneratorExp(
elt=node.elt,
generators=node.generators
)
return gen_exp
def _add_external_memory_sort(self, node: ast.Call, opp: OptimizationOpportunity) -> ast.Call:
"""Replace sort with external memory sort"""
self.applied.append(opp)
# Create external sort call
# In practice, would import and use actual external sort implementation
new_call = ast.parse("external_sort(data, buffer_size=int(np.sqrt(len(data))))").body[0].value
return new_call
def _add_cache_blocking(self, node: ast.Call, opp: OptimizationOpportunity) -> ast.Call:
"""Add cache blocking to matrix operations"""
self.applied.append(opp)
# Create blocked matrix multiply call
# In practice, would use optimized implementation
new_call = ast.parse("blocked_matmul(A, B, block_size=64)").body[0].value
return new_call
class SpaceTimeCompiler:
"""Main compiler interface"""
def __init__(self):
self.analyzer = SpaceTimeAnalyzer()
def analyze_code(self, code: str) -> List[OptimizationOpportunity]:
"""Analyze code for optimization opportunities"""
tree = ast.parse(code)
self.analyzer.visit(tree)
return self.analyzer.opportunities
def analyze_file(self, filename: str) -> List[OptimizationOpportunity]:
"""Analyze Python file for optimization opportunities"""
with open(filename, 'r') as f:
code = f.read()
return self.analyze_code(code)
def analyze_function(self, func) -> List[OptimizationOpportunity]:
"""Analyze function object for optimization opportunities"""
source = inspect.getsource(func)
return self.analyze_code(source)
def transform_code(self, code: str,
opportunities: Optional[List[OptimizationOpportunity]] = None,
auto_select: bool = True) -> TransformationResult:
"""Transform code to apply optimizations"""
# Parse code
tree = ast.parse(code)
# Analyze if opportunities not provided
if opportunities is None:
analyzer = SpaceTimeAnalyzer()
analyzer.visit(tree)
opportunities = analyzer.opportunities
# Select which opportunities to apply
if auto_select:
selected = self._auto_select_opportunities(opportunities)
else:
selected = opportunities
# Apply transformations
transformer = SpaceTimeTransformer(selected)
optimized_tree = transformer.visit(tree)
# Generate optimized code
optimized_code = ast.unparse(optimized_tree)
# Add necessary imports
imports = self._get_required_imports(transformer.applied)
if imports:
optimized_code = imports + "\n\n" + optimized_code
# Calculate overall impact
total_memory_reduction = 0
total_time_overhead = 0
if transformer.applied:
total_memory_reduction = np.mean([opp.memory_savings for opp in transformer.applied])
total_time_overhead = np.mean([opp.time_overhead for opp in transformer.applied])
return TransformationResult(
original_code=code,
optimized_code=optimized_code,
opportunities_found=opportunities,
opportunities_applied=transformer.applied,
estimated_memory_reduction=total_memory_reduction,
estimated_time_overhead=total_time_overhead
)
def _auto_select_opportunities(self,
opportunities: List[OptimizationOpportunity]) -> List[OptimizationOpportunity]:
"""Automatically select which optimizations to apply"""
selected = []
for opp in opportunities:
# Apply if high confidence and good tradeoff
if opp.confidence > 0.7:
if opp.memory_savings > 50 and opp.time_overhead < 100:
selected.append(opp)
elif opp.time_overhead < 0: # Performance improvement
selected.append(opp)
return selected
def _get_required_imports(self,
applied: List[OptimizationOpportunity]) -> str:
"""Get import statements for applied optimizations"""
imports = set()
for opp in applied:
if opp.type == OptimizationType.CHECKPOINT:
imports.add("import numpy as np")
imports.add("from checkpointing import checkpoint_data")
elif opp.type == OptimizationType.EXTERNAL_MEMORY:
imports.add("import numpy as np")
imports.add("from external_memory import external_sort")
elif opp.type == OptimizationType.CACHE_BLOCKING:
imports.add("from optimized_ops import blocked_matmul")
return "\n".join(sorted(imports))
def compile_file(self, input_file: str, output_file: str,
report_file: Optional[str] = None):
"""Compile Python file with space-time optimizations"""
print(f"Compiling {input_file}...")
# Read input
with open(input_file, 'r') as f:
code = f.read()
# Transform
result = self.transform_code(code)
# Write output
with open(output_file, 'w') as f:
f.write(result.optimized_code)
# Generate report
if report_file or result.opportunities_applied:
report = self._generate_report(result)
if report_file:
with open(report_file, 'w') as f:
f.write(report)
else:
print(report)
print(f"Optimized code written to {output_file}")
if result.opportunities_applied:
print(f"Applied {len(result.opportunities_applied)} optimizations")
print(f"Estimated memory reduction: {result.estimated_memory_reduction:.1f}%")
print(f"Estimated time overhead: {result.estimated_time_overhead:.1f}%")
def _generate_report(self, result: TransformationResult) -> str:
"""Generate optimization report"""
report = ["SpaceTime Compiler Optimization Report", "="*60, ""]
# Summary
report.append(f"Opportunities found: {len(result.opportunities_found)}")
report.append(f"Optimizations applied: {len(result.opportunities_applied)}")
report.append(f"Estimated memory reduction: {result.estimated_memory_reduction:.1f}%")
report.append(f"Estimated time overhead: {result.estimated_time_overhead:.1f}%")
report.append("")
# Details of opportunities found
if result.opportunities_found:
report.append("Optimization Opportunities Found:")
report.append("-"*60)
for i, opp in enumerate(result.opportunities_found, 1):
applied = "✓" if opp in result.opportunities_applied else "✗"
report.append(f"{i}. [{applied}] Line {opp.line_number}: {opp.type.value}")
report.append(f" {opp.description}")
report.append(f" Memory savings: {opp.memory_savings:.1f}%")
report.append(f" Time overhead: {opp.time_overhead:.1f}%")
report.append(f" Confidence: {opp.confidence:.2f}")
report.append("")
# Code comparison
if result.opportunities_applied:
report.append("Code Changes:")
report.append("-"*60)
report.append("See output file for transformed code")
return "\n".join(report)
# Decorator for automatic optimization
def optimize_spacetime(memory_limit: Optional[int] = None,
time_constraint: Optional[float] = None):
"""Decorator to automatically optimize function"""
def decorator(func):
# Get function source
source = inspect.getsource(func)
# Compile with optimizations
compiler = SpaceTimeCompiler()
result = compiler.transform_code(source)
# Create new function from optimized code
# This is simplified - real implementation would be more robust
namespace = {}
exec(result.optimized_code, namespace)
# Return optimized function
optimized_func = namespace[func.__name__]
optimized_func._spacetime_optimized = True
optimized_func._optimization_report = result
return optimized_func
return decorator
# Example functions to demonstrate compilation
def example_sort_function(data: List[float]) -> List[float]:
"""Example function that sorts data"""
n = len(data)
sorted_data = sorted(data)
return sorted_data
def example_accumulation_function(n: int) -> float:
"""Example function with accumulation"""
total = 0.0
values = []
for i in range(n):
value = i * i
values.append(value)
total += value
return total
def example_matrix_function(A: np.ndarray, B: np.ndarray) -> np.ndarray:
"""Example matrix multiplication"""
C = np.dot(A, B)
return C
def example_comprehension_function(n: int) -> List[int]:
"""Example with large list comprehension"""
squares = [i * i for i in range(n)]
return squares
def demonstrate_compilation():
"""Demonstrate the compiler"""
print("SpaceTime Compiler Demonstration")
print("="*60)
compiler = SpaceTimeCompiler()
# Example 1: Analyze sorting function
print("\n1. Analyzing sort function:")
print("-"*40)
opportunities = compiler.analyze_function(example_sort_function)
for opp in opportunities:
print(f" Line {opp.line_number}: {opp.description}")
print(f" Potential memory savings: {opp.memory_savings:.1f}%")
# Example 2: Transform accumulation function
print("\n2. Transforming accumulation function:")
print("-"*40)
source = inspect.getsource(example_accumulation_function)
result = compiler.transform_code(source)
print("Original code:")
print(source)
print("\nOptimized code:")
print(result.optimized_code)
# Example 3: Matrix operations
print("\n3. Optimizing matrix operations:")
print("-"*40)
source = inspect.getsource(example_matrix_function)
result = compiler.transform_code(source)
for opp in result.opportunities_applied:
print(f" Applied: {opp.description}")
# Example 4: List comprehension
print("\n4. Converting list comprehension:")
print("-"*40)
source = inspect.getsource(example_comprehension_function)
result = compiler.transform_code(source)
if result.opportunities_applied:
print(f" Memory reduction: {result.estimated_memory_reduction:.1f}%")
print(f" Converted to generator expression")
def main():
"""Main entry point for command-line usage"""
import argparse
parser = argparse.ArgumentParser(description='SpaceTime Compiler')
parser.add_argument('input', help='Input Python file')
parser.add_argument('-o', '--output', help='Output file (default: input_optimized.py)')
parser.add_argument('-r', '--report', help='Generate report file')
parser.add_argument('--analyze-only', action='store_true',
help='Only analyze, don\'t transform')
parser.add_argument('--demo', action='store_true',
help='Run demonstration')
args = parser.parse_args()
if args.demo:
demonstrate_compilation()
return
compiler = SpaceTimeCompiler()
if args.analyze_only:
# Just analyze
opportunities = compiler.analyze_file(args.input)
print(f"\nFound {len(opportunities)} optimization opportunities:")
print("-"*60)
for i, opp in enumerate(opportunities, 1):
print(f"{i}. Line {opp.line_number}: {opp.type.value}")
print(f" {opp.description}")
print(f" Memory savings: {opp.memory_savings:.1f}%")
print(f" Time overhead: {opp.time_overhead:.1f}%")
print()
else:
# Compile
output_file = args.output or args.input.replace('.py', '_optimized.py')
compiler.compile_file(args.input, output_file, args.report)
if __name__ == "__main__":
main()