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Joint Learning the Cache and Prefetcher

Abstract

Modern computing environments are characterized by a wide variety of applications, each with unique access patterns and requirements. Heuristics and static solutions often operate inefficiently on this diverse ranperformancege of workloads, leading to the development of many learned components in OS which can better adapt to unique applications.

Most ML-based solutions in the OS focus around optimizing individual components, where each component implements a separate model trained on its own featureset. However, prior insight points to the idea that an ML solution beyond a single component can give better decisions.

Almost all components in the OS are interdependent in one way or another, where decisions from one component can affect the state or performance of another. If informed about the actions of an interdependent component, a model could then make better, adaptive decisions.

This project aims to create a joint learner solution that facilitates a holistic understanding of relationships between OS components to make more informed decisions that consider the effect of one mechanism on another.

Instructions for Training Models

Using ChampSim to Collect Data Traces

  1. Enter the sim/ folder
cd sim
  1. Set up the simulator by downloading dependencies:
git submodule update --init
vcpkg/bootstrap-vcpkg.sh
vcpkg/vcpkg install

ChampSim takes a JSON configuration script. Use champsim_config.json as a working example.

./config.sh <configuration file>
make

  1. Download SPEC Benchmark traces from this site

  2. Run the simulator, e.g.

bin/champsim --warmup_instructions 50000000 --simulation_instructions 50000000 --name sphinx ~/path/to/traces/482.sphinx3-234B.champsimtrace.xz

The number of warmup and simulation instructions given will be the number of instructions retired.

Data Preprocessing

In order to add labels to cache instructions for whether an instruction would be cached under Belady's OPT, run the add_labels.py script from the root directory, e.g.

python src/data_engineering/add_labels.py --input ~/path/to/simtraces/cache_accesses_sphinx.csv --output ~/path/to/output.csv --cache_size 4096

The size of the cache is variable, and ChampSim's cache size is configurable in the champsim_config.json. The default is 2048 * 16, or the number of sets * number of ways in the LLC cache.

Model Training

To train the joint model, firstly constrastively train the encoders using through the train_embedders.py script:

CUDA_VISIBLE_DEVICES=1 python src/train_embedders.py --prefetch_data_path ~path/to/labeled_data/prefetches_sphinx.csv --cache_data_path ~/path/to/labeled_data/cache_accesses_sphinx.csv --model_name contrastive_encoder_sphinx -l 0.0001 --config ./configs/base_voyager_cont.yaml

The models should be saved to the ./data/model/ folder.

To train the cache replacement model individually, use train_mlp.py:

CUDA_VISIBLE_DEVICES=1 python src/train_mlp.py --ip_history_window 10 --batch_size 256 --model_name mlp_sphinx --cache_data_path ~/path/to/labeled_data/labeled_cache_sphinx.csv

Use the encoder_name flag to use the contrastively trained encoder:

CUDA_VISIBLE_DEVICES=1 python src/train_mlp.py --ip_history_window 10 --batch_size 256 --model_name mlp_sphinx --cache_data_path ~/path/to/labeled_data/labeled_cache_sphinx.csv --encoder_name contrastive_encoder_sphinx

To train the voyage prefetcher model, firstly download the traces from this link for the appropriate benchmark (using the ChampSim trace instead is WIP), and use train_voyager.py:

CUDA_VISIBLE_DEVICES=1 python src/train_voyager.py --prefetch_data_path ~/path/to/data/482.sphinx3-s0.txt.xz --model_name voyager_base

Or, specify the encoder_name to use the contrastively trained encoder as above.