---
title: "Starving the GPU"
subtitle: "Your GPU can process 5,300 images per second. Your CPU decodes 850."
description: "Discover that the data pipeline — not the GPU — is often the binding constraint in training. Use DataModel and TransformationModel to find the crossover where CPU preprocessing stalls the accelerator."
categories: ["data", "intermediate"]
---

## The Question

You launch ResNet-50 training on an A100 and watch `nvidia-smi`. GPU utilization reads 40%.
You expected 95%. The model is compute-bound. The hardware is top-tier. **Why is your GPU
sitting idle 60% of the time?**

The answer is almost never the model or the GPU. It is the invisible pipeline upstream:
JPEG decoding, random cropping, color jitter, and normalization — all running on the CPU.
When the CPU cannot prepare batches fast enough, the GPU starves.

::: {.callout-note}
## Prerequisites
Complete [Tutorial 0: Hello, Roofline](00_hello_roofline.qmd) and
[Tutorial 1: The Memory Wall](01_memory_wall.qmd). You should understand memory-bound
vs. compute-bound regimes and how `Engine.solve` reports bottlenecks.
:::

::: {.callout-note}
## What You Will Learn

- **Measure** the GPU's step time in isolation using `SingleNodeModel`
- **Calculate** the data pipeline's throughput using `DataModel` and `TransformationModel`
- **Identify** the batch size crossover where the CPU becomes the binding constraint
- **Predict** how many CPU workers are needed to eliminate the data bottleneck
:::

::: {.callout-tip}
## Background: The Three Stages of a Training Step

Every training step has three sequential stages. The slowest one determines your actual
throughput — not the GPU alone:

1. **Storage I/O** (Wall 8) — Read raw data from disk into CPU memory
2. **CPU Preprocessing** (Wall 9) — Decode, resize, augment, normalize
3. **Accelerator Compute** (Wall 1) — Forward pass, backward pass, weight update

The GPU cannot start until stages 1 and 2 finish. If either is slower than the GPU, the
accelerator utilization drops below 100%. This is the data pipeline bottleneck.
:::

---

## 1. Setup

```{python}
#| echo: false
#| output: false
import mlsysim  # installed via `pip install mlsysim` (see workflow)
Engine = mlsysim.Engine
```

```python
import mlsysim
from mlsysim.solvers import SingleNodeModel, DataModel
from mlsysim.solvers import TransformationModel
```

---

## 2. GPU Compute Time: The Ceiling You Think You Have

We switch from LLM serving (Tutorials 2–3) to **CNN training** because the data pipeline
bottleneck is most visible here. LLM training on tokenized text has a tiny data footprint
(~8 MB/s as we will see in [Tutorial 12](12_full_stack_audit.qmd)). Image training with
JPEG decoding, resizing, and augmentation can demand 10–100× more CPU work per sample —
this is where the GPU actually starves.

First, establish how fast the A100 processes a ResNet-50 training step in isolation — no
data loading, no preprocessing, just pure compute:

```{python}
from mlsysim.solvers import SingleNodeModel
from mlsysim.core.units import Q_
from mlsysim.show import table, info

model = mlsysim.Models.Vision.ResNet50
hardware = mlsysim.Hardware.Cloud.A100
solver = SingleNodeModel()

# Baseline: ResNet-50 on A100, batch 256, FP16
profile = solver.solve(model=model, hardware=hardware, batch_size=256, precision="fp16")

info("GPU Compute Baseline",
     Model=model.name,
     Hardware=hardware.name,
     Batch_size=256,
     Step_latency=profile.latency.to('ms'),
     Throughput=f"{profile.throughput:.0f} img/s",
     Bottleneck=profile.bottleneck)
```

The GPU can process this batch in tens of milliseconds. That is the ceiling. Now let's
check whether the data pipeline can keep up.

---

## 3. Storage I/O Check: Can the Disk Deliver?

ImageNet images average ~500 KB each (JPEG compressed). At batch 256, the GPU demands a
burst of data every step. Can the storage subsystem supply it?

```{python}
from mlsysim.solvers import DataModel

sample_size = Q_("500 KB")  # Average ImageNet JPEG
batch_size = 256

# Data demand = batch_size x sample_size / step_time
step_time_s = profile.latency.to("s").magnitude
data_per_step = (batch_size * sample_size.to("GB")).magnitude
demand_rate = Q_(data_per_step / step_time_s, "GB/s")

data_solver = DataModel()
data_result = data_solver.solve(workload_data_rate=demand_rate, hardware=hardware)

info("Storage I/O Check",
     Data_demand=f"{demand_rate:.3f}",
     Storage_supply=f"{data_result.supply_bw:.2f}",
     Utilization=f"{data_result.utilization:.1%}",
     Is_stalled=data_result.is_stalled)
```

Storage I/O is fine — modern NVMe SSDs can deliver multi-GB/s easily. The bottleneck is
not reading the bytes. It is *transforming* them.

---

## 4. The Reveal: CPU Preprocessing Is the Wall

Even with fast storage, the CPU must decode JPEGs, apply random crops, color jitter, and
normalization. A typical CPU worker processes ImageNet images at ~250 MB/s. With 8 workers,
total CPU throughput is ~2 GB/s:

```{python}
from mlsysim.solvers import TransformationModel

transform_solver = TransformationModel()
cpu_throughput = Q_("2 GB/s")  # 8 workers x 250 MB/s each

t = transform_solver.solve(
    batch_size=256,
    sample_size_bytes=sample_size,
    cpu_throughput=cpu_throughput,
    accelerator_step_time=profile.latency
)

info("CPU vs GPU Pipeline",
     CPU_transform_time=t.transform_time,
     GPU_step_time=t.accelerator_step_time,
     CPU_is_bottleneck=t.is_bottleneck,
     GPU_utilization=f"{t.accelerator_utilization:.1%}",
     Slowdown_factor=f"{t.slowdown_factor:.2f}x")
```

::: {.callout-important}
## Key Insight

**The binding constraint is not silicon — it is JPEG decoding on the CPU.** The data
pipeline (Wall 9: Transformation) becomes the bottleneck before the GPU (Wall 1: Compute).
Your GPU can process 5,300+ images per second, but your 8 CPU workers can only prepare
~850. The GPU sits idle waiting for data. This is why production training pipelines use
GPU-accelerated preprocessing (NVIDIA DALI), pre-decoded datasets, or aggressive
prefetching.
:::

---

## 5. Batch Size Sweep: Finding the Crossover

Let's sweep batch sizes to find exactly where the CPU becomes the binding constraint. At
small batches, the GPU is slower and data arrives in time. At large batches, the GPU
becomes more efficient but the CPU falls behind:

```{python}
rows = []
for bs in [32, 64, 128, 256, 512, 1024]:
    p = solver.solve(model=model, hardware=hardware, batch_size=bs, precision="fp16")

    t = transform_solver.solve(
        batch_size=bs,
        sample_size_bytes=sample_size,
        cpu_throughput=cpu_throughput,
        accelerator_step_time=p.latency
    )

    binding = "Transformation" if t.is_bottleneck else p.bottleneck
    rows.append([
        bs,
        f"{p.latency.to('ms').magnitude:.2f} ms",
        f"{t.transform_time.to('ms').magnitude:.2f} ms",
        binding,
        f"{t.accelerator_utilization:.1%}"
    ])

table(["Batch", "GPU Step", "CPU Xform", "Binding", "GPU Util"], rows)
```

Watch the crossover: at small batch sizes the GPU is the bottleneck (100% utilization).
As batch size grows, CPU preprocessing time grows linearly while GPU step time grows
sub-linearly. Eventually Wall 9 becomes the binding constraint and GPU utilization drops.

---

## 6. The Fix: Adding CPU Workers

The simplest fix for a CPU bottleneck is more workers. Let's compare 8 vs. 16 vs. 32:

```{python}
rows = []
for n_workers in [8, 16, 32]:
    cpu_tp = Q_(f"{n_workers * 250} MB/s")

    p = solver.solve(model=model, hardware=hardware, batch_size=512, precision="fp16")

    t = transform_solver.solve(
        batch_size=512,
        sample_size_bytes=sample_size,
        cpu_throughput=cpu_tp,
        accelerator_step_time=p.latency
    )

    rows.append([n_workers, cpu_tp.to('GB/s'), f"{t.accelerator_utilization:.1%}"])

table(["Workers", "Throughput", "GPU Util @ bs=512"], rows)
```

Doubling workers doubles throughput — but you eventually hit either storage I/O limits
(Wall 8) or PCIe bandwidth. The takeaway: always check *all three stages* of the pipeline.

---

## Your Turn

::: {.callout-caution}
## Exercises

**Exercise 1: Predict before you compute.**
At batch size 64 with 8 CPU workers (2 GB/s total), will ResNet-50 training on the A100
be GPU-bound or CPU-bound? Write your prediction, then run the code. What determines the
answer? (Hint: compare `transform_time` vs. `accelerator_step_time`.)

**Exercise 2: Medical imaging — larger samples.**
Medical imaging uses images 10x larger than ImageNet (~5 MB per sample). Change
`sample_size` to `Q_("5 MB")` and re-run the batch size sweep. At what batch size does
the CPU stall the GPU now? How many workers would you need to keep up at batch 256?

**Exercise 3: GPU-accelerated preprocessing.**
If you use NVIDIA DALI to move preprocessing to the GPU, the CPU bottleneck effectively
disappears. Model this by setting `cpu_throughput = Q_("50 GB/s")`. Run the sweep again.
Does the bottleneck shift back to compute? What is the new GPU utilization at batch 512?

**Self-check:** If the GPU step takes 20 ms and CPU preprocessing takes 35 ms, what is the
accelerator utilization? (Answer: 20/35 = 57%.)
:::

---

## Key Takeaways

::: {.callout-tip}
## Summary

- **Data pipelines have three stages**: storage I/O, CPU preprocessing, and GPU compute — the slowest determines throughput
- **CPU preprocessing (Wall 9)** is the most common bottleneck: JPEG decode, augmentation, and normalization are all CPU-bound
- **Batch size shifts the binding constraint**: small batches are GPU-bound; large batches often become CPU-bound
- **Adding CPU workers** helps linearly but has diminishing returns when storage I/O becomes the limit
- **Always check all three stages** before concluding that the GPU is the bottleneck
:::

---

## Next Steps

- **[Quantization: Not a Free Lunch](05_quantization.qmd)** — When reducing precision helps (and when it doesn't)
- **[KV-Cache: The Hidden Tax](03_kv_cache.qmd)** — Another hidden memory consumer: the KV-cache in LLM serving
- **[Where to Invest](09_sensitivity.qmd)** — Use sensitivity analysis to decide whether more CPU workers or a faster GPU is the better investment
- **[Silicon Zoo](../zoo/hardware.qmd)** — Compare storage and interconnect specs across GPU platforms
