'I paid for the whole GPU, I am going to use the whole GPU'
a year ago
- #GPU
- #Machine Learning
- #Performance Optimization
- GPUs are specialized co-processors designed for high-throughput mathematical operations, particularly matrix multiplications, where CPUs fall short.
- GPU utilization is a critical concern due to their high cost, with different aspects of utilization including Allocation, Kernel, and Model FLOP/s Utilization.
- GPU Allocation Utilization measures the fraction of GPU time spent running application code versus idle time, influenced by economic and operational factors.
- Modal helps improve GPU Allocation Utilization by aggregating demand and supply across clouds, reducing latency in spinning up GPUs for application use.
- GPU Kernel Utilization refers to the time GPUs spend executing kernels (GPU code), with low utilization often due to host overhead or insufficient work provisioning.
- Model FLOP/s Utilization (MFU) measures the efficiency of using the GPU's theoretical arithmetic bandwidth, with high MFU requiring optimized kernels and memory usage.
- Achieving high MFU is challenging, with state-of-the-art training runs achieving 20-41% MFU, while inference may reach higher efficiencies.
- Improving GPU utilization involves optimizing application code, reducing host overhead, using efficient kernels, and leveraging platforms like Modal for better allocation.