Optimizing Energy Efficiency in Transformer-based Models

Auto-tuning GPU Kernels for Sustainable AI Inference

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Overview

This project focuses on optimizing kernels for transformer architectures (LLAMA, GPT-2) through automated GPU kernel tuning with energy-aware metrics. By combining performance engineering techniques like power capping with hardware telemetry from modern NVIDIA GPUs, we identify Pareto-optimal configurations that balance computational throughput and energy efficiency.

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Technical Implementation

Component	Purpose
HeCBench	Baseline CUDA kernels for attention mechanisms
Kernel Tuner	Automated GPU kernel parameter optimization
NVMETRICS	nvmetrics is a library to measure GPU metrics using NVIDIA CUPTI
NVIDIA Nsight Compute	Fine-grained SM occupancy profiling
NVML Power Monitoring	Real-time energy consumption tracking
GPT-2/LLAMA Model cuda implementation	One of Karpathy's llm.c forks

Research Questions

1. Occupancy-Energy Tradeoffs in Attention Mechanisms

Q: What's the optimal balance between SM occupancy and energy-per-token in LLM attention layers?
Approach: Systematic profiling of block dimensions (16x16 to 256x4) using Kernel Tuner with NVML/Nsight metrics . Establishes baseline for transformer's most compute-intensive operation (40-60% of FLOPs)
Novelty: Empirical study of occupancy-energy Pareto fronts in real-world sequence lengths (256-4096 tokens)
Impact: Challenges maximal occupancy dogma, validate energy savings via optimized block shapes

2. Memory Coalescing for Efficient Embeddings

Q: Can block tuning achieve 3× energy reduction in embeddings through L2 cache optimization?
Approach: Energy-aware coalescing metric combining cache lines accessed and coalesce factor per joule
Novelty: Quantifies strided memory vs. SM idle power tradeoff without quantization
Impact: Enables energy-efficient embedding layers for large context windows

3. Adaptive Block Sizing for Dynamic Inference

Q: Does real-time sequence-length-aware block adaptation reduce decoding energy vs static configurations?
Approach: MLP-based selector trained on Kernel Tuner profiles with sequence length detection
Novelty: First implementation of lightweight neural block size predictor for dynamic inference
Impact: Enables energy-proportional decoding for variable-length sequences

4. Compute vs Memory-Bound Kernel Behavior

Q: How do LLM kernel types (compute-bound and memory-bound kernels) fundamentally differ in energy/power characteristics under varying block configurations(exhibit divergent energy scaling laws)?
Approach:

• Classify kernels using arithmetic intensity thresholds
• Comparative analysis of 10+ metrics (energy, power variance, cache efficiency) across kernel types
  Novelty: First taxonomy of energy scaling patterns based on kernel resource utilization
  Impact: Enables targeted optimizations - thread coarsening for compute-bound vs shared mem tuning for memory-bound

5. Predictive Energy Modeling with Power Capping

Q: Can hybrid features (static code features + runtime metrics) enable intelligent power capping for LLM workloads?
Approach:

• Hybrid XGBoost model combining code complexity scores and real-time telemetry
• Power capping trials based on model predictions
  Novelty: First end-to-end system adapting power limits using kernel characteristics
  Impact: Achieves 18-22% energy savings via predictive power management

Getting Started

Dependencies

# Core Requirements
CUDA 12.4+
Python 3.11+
NVIDIA Driver 550+
NVCC 12.4+

Compiler backend : Pycuda or CuPy, based on the device code. PyCuda uses NVCC which need device and host code separately. CuPy takes by default cuda c and single script works. 

Note : If getting "extern C linkage error, pass the whole code wrapped in extern "C" {.....} , or use the manged kernel name instead of the readable one.

# Python Packages
pip install kernel-tuner cupy-cuda12x pytorch matplotlib pandas

Basic Usage

from kernel_tuner import tune_kernel

results = tune_kernel(
    kernel_name="mha",
    kernel_source="attentionMultiHead.cu",
    problem_size=(num_heads*batch_size, 1),
    tune_params=tune_params,
    observers=[NVMLObserver(), NCUObserver()]
)

restrictions = [
        "block_size_x * block_size_y % 32 == 0", 
        "block_size_x * block_size_y <= 1024",
        f"(({dim_feature} // {nhead}) % block_size_x) == 0",
        f"shared_size_factor * (({dim_feature}//{nhead}) + {n_steps}) * 4 <= 48*1024"
    ]

Results & Metrics

Key Performance Indicators

Metric	Formula	Target
Energy Efficiency	Total Energy / (Tokens Processed)	Minimize
Compute Intensity	(FLOPS) / (Watt)	Maximize
SM Occupancy	Active Warps / Maximum Warps	≥80%
I'll add more as i move along with the RQs	...	...

Hardware Configuration

Component	Specification
GPU	NVIDIA RTX 5000 Ada (24GB VRAM)
CPU	AMD EPYC 9554P 64-Core Processor
Memory	1TB DDR4 @ 3200MHz
Power Sampling	1ms resolution via NVML