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Performance Optimization
Measure · Analyze · Optimize · Validate
A comprehensive engineering guide to measuring, analyzing, and improving EveryO model performance while maintaining numerical correctness, reproducibility, and reliability.
Home · Tensor Engine · Backend System · Model Training · API Reference
Maintained By & Owner: EveryO Contributors & @krishanth7
Important
Performance and Compatibility
This guide is based on inspected EveryO source interfaces, including everyo.profiler, everyo.core.device, and everyo.data.dataloader.
Benchmark numbers must be measured on actual hardware. The examples do not imply guaranteed speedups, GPU availability, or specific performance results.
- Overview
- Performance Engineering Architecture
- Performance Metrics
- Profiling API
- Profiling a Neural Network
- Custom Profiling Regions
- Chrome Trace Export
- CPU Execution
- CUDA Execution
- Tensor Computation Efficiency
- Memory Optimization
- DataLoader Performance
- Batch Size Optimization
- Training Throughput
- Inference Optimization
- Numerical Precision
- Benchmarking Methodology
- Complete Benchmark Example
- Performance Regression Testing
- Bottleneck Analysis
- Reproducibility
- Troubleshooting
- Optimization Checklist
- Developer References
- Next Steps
Performance optimization improves the computational efficiency of machine learning systems while preserving acceptable numerical behavior.
EveryO performance depends on model architecture, tensor operations, execution devices, data pipelines, and Python runtime overhead.
- Reduce execution latency.
- Improve training throughput.
- Minimize unnecessary allocations.
- Identify computational bottlenecks.
- Measure memory consumption.
- Compare execution backends.
- Preserve numerical correctness.
- Detect performance regressions.
- Establish a baseline.
- Measure representative workloads.
- Identify the dominant bottleneck.
- Change one variable at a time.
- Re-measure performance.
- Verify numerical correctness.
- Record the results.
EveryO Workload
|
v
Baseline Benchmark
|
v
Profiling
|
v
Bottleneck Analysis
|
+---------+----------+
| | |
v v v
Compute Memory Data Input
| | |
+---------+----------+
|
v
Targeted Changes
|
v
Re-Benchmark
|
v
Numerical Validation
|
v
Regression Testing
| Layer | Optimization Focus |
|---|---|
| Model | Architecture and parameter count |
| Tensor | Operation shapes and allocations |
| Backend | Supported CPU/CUDA execution |
| Data | Batching and preprocessing |
| Training | Gradient and optimizer overhead |
| Inference | Evaluation-mode execution |
| Runtime | Python and system overhead |
Performance should be evaluated using measurable metrics.
Latency measures the time required to complete an operation.
[ L=t_{end}-t_{start} ]
Common units:
- Nanoseconds.
- Microseconds.
- Milliseconds.
- Seconds.
Throughput measures work completed per unit of time.
[ T=\frac{N}{t} ]
where:
- (N) = processed samples.
- (t) = elapsed seconds.
Unit:
samples/second
[ S=\frac{T_{baseline}}{T_{optimized}} ]
Here, (T) represents execution time for the same workload.
A speedup greater than 1 indicates lower execution time.
Memory measurements may include:
- Model parameter memory.
- Intermediate tensor memory.
- Gradient memory.
- Optimizer state.
- Dataset memory.
- Runtime overhead.
| Metric | Unit | Desired Direction |
|---|---|---|
| Latency | ms | Lower |
| Throughput | samples/s | Higher |
| Peak memory | MiB | Lower |
| Time per epoch | s | Lower |
| Error versus reference | Task-dependent | Within tolerance |
EveryO provides an opt-in profiler.
from everyo.profiler import (
ProfileEvent,
Profiler,
profile,
record_function,
)Profiler(
*,
warmup=0,
record_shapes=True
)| Parameter | Purpose |
|---|---|
warmup |
Number of initial recorded events to skip |
record_shapes |
Include input shape metadata |
Note
The inspected implementation's warmup counter applies to profiling events, not complete training iterations.
| Method | Description |
|---|---|
summary() |
Aggregated timing information |
export_json(path) |
Raw event export |
export_chrome_trace(path) |
Chrome/Perfetto trace export |
record(name, metadata) |
Custom timing region |
Each aggregated summary row contains:
name
calls
total_ms
mean_ms
max_ms
import everyo as eo
from everyo.profiler import profile
model = eo.Sequential(
eo.Linear(4, 16, seed=0),
eo.ReLU(),
eo.Linear(16, 2, seed=1)
)
x = eo.zeros(32, 4)
with profile(record_shapes=True) as profiler:
for _ in range(20):
output = model(x)
for row in profiler.summary():
print(row)The profiler records module execution intervals.
It can expose timing information for:
- Sequential containers.
- Linear layers.
- Activation layers.
- Other module calls.
Nested module timing intervals overlap.
Do not add parent and child total durations together and interpret the result as total wall-clock execution time.
If a layer accounts for a large proportion of measured execution time, investigate:
- Input shape.
- Parameter count.
- Operation complexity.
- Backend behavior.
- Repeated allocations.
The record_function() context manager records user-defined operations while a profiler is active.
import everyo as eo
from everyo.profiler import (
profile,
record_function,
)
model = eo.Sequential(
eo.Linear(4, 8, seed=0),
eo.ReLU(),
eo.Linear(8, 2, seed=1)
)
x = eo.zeros(64, 4)
with profile() as profiler:
with record_function(
"inference",
batch_size=64
):
result = model(x)
print(profiler.summary())Custom metadata can include:
- Batch size.
- Experiment name.
- Input dimensions.
- Processing stage.
Use JSON-serializable metadata when exporting traces.
When no profiler is active, record_function() acts as a normal context manager without recording an event.
EveryO can export profiling events for timeline visualization.
from pathlib import Path
from everyo.profiler import profile
Path("profiles").mkdir(exist_ok=True)
with profile() as profiler:
output = model(x)
profiler.export_chrome_trace(
"profiles/model-trace.json"
)The exported trace follows a format suitable for Chrome-compatible tracing viewers, including Perfetto.
- Long-running operations.
- Repeated module calls.
- Nested execution.
- Timing variance.
- Custom workload regions.
profiler.export_json(
"profiles/raw-events.json"
)EveryO uses NumPy for CPU tensor execution.
from everyo.core.device import device
cpu = device("cpu")
print(cpu)Expected output:
cpu
CPU performance depends on:
- Processor architecture.
- Memory bandwidth.
- NumPy configuration.
- Array dimensions.
- Operation type.
- Runtime overhead.
- Prefer batch operations over Python loops.
- Avoid repeated conversions.
- Reuse data where practical.
- Use appropriate array dtypes.
- Measure the impact of shape changes.
EveryO includes a CUDA execution-device abstraction.
from everyo.core.device import device
execution_device = device("auto")
print(execution_device)The auto specification selects CUDA when the supported CUDA extension is available, otherwise CPU.
execution_device = device("cuda")When CUDA is unavailable, the default behavior falls back to CPU with a warning.
execution_device = device(
"cuda",
strict=True
)Strict mode raises an error if CUDA is unavailable.
The inspected device implementation states that tensor buffers remain in host memory as NumPy arrays.
The device determines where supported operations execute.
Therefore, selecting CUDA does not mean every tensor is permanently stored in GPU memory or every operation is accelerated.
Compare:
- CPU latency.
- CUDA latency.
- Numerical outputs.
- Operation coverage.
- Transfer overhead.
- Batch size sensitivity.
Never assume CUDA is faster for every workload.
Tensor performance is strongly influenced by shapes and operation selection.
For matrices:
[ A\in\mathbb{R}^{m\times k} ]
and:
[ B\in\mathbb{R}^{k\times n} ]
the conventional arithmetic complexity is:
[ O(mkn) ]
import everyo as eo
a = eo.ones(128, 256)
b = eo.ones(256, 64)
result = eo.matmul(a, b)
print(result.shape)Expected shape:
(128, 64)
- Use vectorized operations.
- Avoid unnecessary intermediate tensors.
- Select suitable dimensions.
- Reduce repeated calculations.
- Profile expensive matrix operations.
Optimizations must preserve:
- Expected shapes.
- Numerical behavior.
- Gradient correctness where applicable.
- Model output semantics.
Memory requirements grow with tensor dimensions, model size, and training state.
For a dense tensor:
[ M=N\times B ]
where:
- (N) = number of elements.
- (B) = bytes per element.
A float32 tensor containing 1,000,000 elements requires:
4{,}000{,}000\text{ bytes} ]
Approximately:
3.81 MiB
Training can require additional memory for:
- Parameters.
- Gradients.
- Intermediate activations.
- Optimizer state.
- Data batches.
- Reduce unnecessary tensor copies.
- Select appropriate batch sizes.
- Avoid retaining unused computation graphs.
- Reuse preprocessed data.
- Use inference contexts when gradients are unnecessary.
EveryO's DataLoader supports a fast path for ArrayDataset.
For array-backed datasets, the inspected implementation batches samples using NumPy indexing.
For other dataset types, it collects individual samples and applies default_collate.
import numpy as np
import everyo as eo
features = np.random.default_rng(42).normal(
size=(10000, 32)
).astype(np.float32)
targets = np.zeros(
10000,
dtype=np.int64
)
dataset = eo.ArrayDataset(
features,
targets
)
loader = eo.DataLoader(
dataset,
batch_size=128,
shuffle=True,
seed=42
)- Use efficient dataset representations.
- Avoid expensive per-sample transformations.
- Select appropriate batch sizes.
- Profile data-loading overhead.
- Keep feature and target arrays aligned.
The inspected DataLoader constructor does not expose a num_workers multiprocessing argument.
Do not assume multiworker loading is supported through this interface.
Batch size affects both memory consumption and throughput.
| Smaller Batches | Larger Batches |
|---|---|
| Lower per-batch memory | Higher per-batch memory |
| More optimizer steps per epoch | Fewer optimizer steps per epoch |
| Different gradient noise characteristics | Different gradient noise characteristics |
| May increase Python overhead | May improve operation utilization |
\left\lceil\frac{N}{B}\right\rceil ]
when the final partial batch is retained.
For:
[ N=10{,}000 ]
and:
[ B=128 ]
the number of batches is:
[ \lceil10000/128\rceil=79 ]
Benchmark several batch sizes while holding other relevant variables constant.
Measure throughput, memory usage, and model behavior.
Training throughput measures the number of samples processed per second.
\frac{SamplesProcessed}{ElapsedSeconds} ]
If 10,000 samples are processed in 20 seconds:
[ Throughput=500\ samples/s ]
This is a mathematical illustration, not an EveryO benchmark result.
- Forward-pass complexity.
- Backward-pass complexity.
- Optimizer overhead.
- Batch size.
- Data loading.
- Hardware.
- Backend implementation.
Record:
- Samples per second.
- Time per epoch.
- Average batch time.
- Validation time.
- Peak memory.
Inference usually does not require gradient computation.
import everyo as eo
model = eo.Sequential(
eo.Linear(4, 8, seed=0),
eo.ReLU(),
eo.Linear(8, 2, seed=1)
)
model.eval()
x = eo.zeros(32, 4)
with eo.no_grad():
predictions = model(x)- Use evaluation mode.
- Disable unnecessary gradient recording.
- Use representative input batches.
- Benchmark end-to-end latency.
- Validate prediction consistency.
A larger batch may improve aggregate throughput while increasing latency for an individual request.
Choose according to application requirements.
Numerical precision affects storage, computation, and accuracy.
| Type | Bytes per Element |
|---|---|
| float16 | 2 |
| float32 | 4 |
| float64 | 8 |
Lower precision may reduce storage requirements but can increase numerical error.
Hardware and backend support determine whether it improves performance.
Use appropriate tolerances:
import numpy as np
np.testing.assert_allclose(
reference,
optimized,
rtol=1e-5,
atol=1e-7
)Do not reduce precision solely for expected speed improvements without measuring correctness and runtime performance.
A trustworthy benchmark requires consistent experimental conditions.
- Define the workload.
- Record hardware and software.
- Warm up the execution path.
- Run repeated measurements.
- Record timing distribution.
- Compare equivalent workloads.
- Validate numerical results.
Report:
- Mean latency.
- Median latency.
- Standard deviation.
- Minimum latency.
- Maximum latency.
- Sample count.
| Field | Example |
|---|---|
| Framework | EveryO |
| Python | Installed version |
| OS | Operating system |
| CPU | Processor model |
| GPU | Device, if used |
| NumPy | Installed version |
| Input shape | (128, 32) |
| Iterations | 100 |
| Warmup | 10 |
The following example measures forward-pass latency using Python's high-resolution timer.
import time
import statistics
import everyo as eo
model = eo.Sequential(
eo.Linear(32, 64, seed=0),
eo.ReLU(),
eo.Linear(64, 16, seed=1)
)
model.eval()
x = eo.zeros(128, 32)
warmup_iterations = 10
measured_iterations = 100
with eo.no_grad():
# Warmup
for _ in range(warmup_iterations):
model(x)
# Timed runs
timings = []
for _ in range(measured_iterations):
start = time.perf_counter()
output = model(x)
elapsed = time.perf_counter() - start
timings.append(elapsed * 1000)
mean_ms = statistics.mean(timings)
median_ms = statistics.median(timings)
minimum_ms = min(timings)
maximum_ms = max(timings)
throughput = 128 / (mean_ms / 1000)
print(f"Mean latency: {mean_ms:.4f} ms")
print(f"Median latency: {median_ms:.4f} ms")
print(f"Minimum latency: {minimum_ms:.4f} ms")
print(f"Maximum latency: {maximum_ms:.4f} ms")
print(f"Approx. throughput: {throughput:.2f} samples/s")This is a simple host-side timing example.
For asynchronous accelerator execution, proper synchronization is required before using host elapsed time as a kernel execution measurement.
The throughput estimate assumes the measured calls represent completed synchronous work.
The script reports measured values from the current environment.
No fixed latency or throughput should be expected.
Performance regression testing identifies unexpected slowdowns.
[ Regression%= \frac{T_{new}-T_{baseline}} {T_{baseline}} \times100 ]
where (T) is execution time for the same workload.
Baseline:
10 ms
New execution:
12 ms
Regression:
[ \frac{12-10}{10}\times100=20% ]
- Run correctness tests on every pull request.
- Execute performance benchmarks on controlled hardware.
- Compare results against stable baselines.
- Allow for measurement noise.
- Investigate significant regressions.
- Record results over time.
Shared CI runners can produce noisy timing measurements.
Use repeated measurements and appropriate thresholds rather than treating every small change as a regression.
Optimization should target the dominant source of execution time.
| Bottleneck | Potential Investigation |
|---|---|
| Matrix multiplication | Dimensions and backend |
| Data loading | Dataset representation |
| Python overhead | Repeated small operations |
| Memory allocation | Intermediate tensors |
| Backpropagation | Computation graph |
| Optimizer updates | Parameter count and state |
| Inference latency | Batch size and architecture |
- Which operations consume the most time?
- Is the workload compute-bound?
- Is memory bandwidth limiting performance?
- Are input batches prepared efficiently?
- Is the selected backend actually available?
- Are results numerically equivalent?
Focus first on operations that contribute substantially to total execution time.
Improving an operation that accounts for only a small fraction of runtime will usually have limited overall impact.
Performance measurements must be reproducible enough to support meaningful comparisons.
- Git commit.
- Framework version.
- Python version.
- NumPy version.
- Hardware.
- Device configuration.
- Input shape.
- Model architecture.
- Benchmark repetitions.
- Warmup policy.
When comparing implementations, keep consistent:
- Input data.
- Batch size.
- Numerical dtype.
- Model parameters.
- Execution mode.
- Benchmark procedure.
Numerical reproducibility and timing reproducibility are different goals.
A model may produce identical predictions while runtime varies because of operating-system scheduling, CPU frequency, caching, or background activity.
Profiler returns no events
Confirm the workload executes inside an active profiler context.
If using warmup, check whether all recorded events were skipped.
CUDA request falls back to CPU
The required CUDA extension may be unavailable.
Run the project's diagnostics and verify the supported CUDA environment.
Use strict device resolution when GPU execution is mandatory.
Benchmark results vary significantly
Increase repetitions, add warmup, reduce background workloads, and report timing distributions.
Large batches cause memory problems
Reduce batch size, model dimensions, or unnecessary intermediate allocations.
Optimization changes numerical results
Check dtype changes, operation order, backend differences, and numerical stability.
Chrome trace file cannot be written
Verify the output directory exists and the process has write permissions.
- Define performance objectives.
- Establish a correctness baseline.
- Record hardware and software.
- Capture baseline latency.
- Capture baseline throughput.
- Identify bottlenecks.
- Change one major variable at a time.
- Measure representative workloads.
- Track memory consumption.
- Check numerical correctness.
- Document findings.
- Run unit tests.
- Run regression tests.
- Reproduce benchmark results.
- Verify supported devices.
- Compare against baseline.
- Document performance limitations.
| Resource | Link |
|---|---|
| EveryO Repository | GitHub |
| Profiler Implementation | profiler.py |
| Device Implementation | device.py |
| Tensor Implementation | tensor.py |
| DataLoader | dataloader.py |
| CUDA Package | cuda/ |
| Training Engine | trainer.py |
| Tests | tests/ |
| Issues | GitHub Issues |
Continue with EveryO's engineering and community documentation.
| Page | Focus |
|---|---|
| Testing and Quality Assurance | Unit testing, CI, and regression testing |
| Contributing | Development contributions |
| Development Setup | Contributor environment |
| Model Training | Training workflows |
| API Reference | Public framework interfaces |
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