PROFET

Analytical model that quantifies the impact of the main memory on application performance.

Supplemental code for the SIGMETRICS 2019 paper "PROFET: Modeling System Performance and Energy Without Simulating the CPU".

The src/profet/ code contains the PROFET model. The bandwdith-latency curves must be precomputed.

Installation

From the project's root folder, install PROFET with pip:

pip install .

It automatically installs any requirements listed in requirements.txt.

After installation, it provides a CLI tool named profet. Additionally, it automatically creates a profet python package that you can use in any of your Python programs.

CLI Tool Usage

Description: The CLI tool is designed to estimate system performance using two memory configurations.

Usage:

profet -a app_profiling_dir -c config_file.json --mem_base bw_lat_curves_dir [OPTIONS] [mem_target]

Arguments:

• -a, --app_profiling: Directory containing application profiling files.
• -c, --config: Configuration file (JSON) path.
• --mem_base: Directory containing bandwidth-latency curves for the baseline system.

Options:

• --mem-target: Directory containing bandwidth-latency dependencies for the target system.
• -b, --benchmarking: Application profiling directory expected to contain subdirectories with the benchmarking app profilings.
• -p, --plot-to-pdf FILE: Specify the location for storing the PDF plot.
• -w, --ignore-warnings: Ignore warning messages.

Arguments Description

• App Profiling: Conducted on the baseline memory system, it involves running the application and profiling with hardware performance counters. This captures data such as CPU cycles, instruction count, last-level cache (LLC) misses, and memory read/write bandwidths. Profiling is sampled over regular intervals, called segments. Refer to Section 4.1 of of the PROFET paper.

• Config: This file contains CPU and DRAM parameters required for PROFET. Configuration JSON examples are available in the configs/ directory.

• Memory Base & Target: Acquired using bandwidth-latency curves for both the baseline and target memory systems. The methodology is detailed in Section 3 of the PROFET paper, which explains the memory profiling microbenchmarks and their results.

Execution Examples

Assuming $PROFETPATH contains the path to this PROFET package.

Baseline Plot

Generate a plot in the current directory (specified by -p .) that provides a summary of the bandwidth-latency for baseline profiling. It uses:

• Profiling data for the HPCG application, located at $PROFETPATH/tests/app_profiles/cascade_lake__DDR4_2666__local_socket/HPCG.
• The configuration for the system, located at $PROFETPATH/configs/sample.json.
• Baseline precomputed memory bandwidth-latency curves, located at $PROFETPATH/data/bw_lat_curves/cascade_lake__DDR4_2666__local_socket/.

profet -w \
-p . \
-a $PROFETPATH/tests/app_profiles/cascade_lake__DDR4_2666__local_socket/HPCG \
-c $PROFETPATH/configs/sample.json \
--mem-base $PROFETPATH/data/bw_lat_curves/cascade_lake__DDR4_2666__local_socket/

Baseline and Target Prediction Plot

Generate a plot, in your current folder, with a bandwidth-latency summary of the baseline profiling and the target system prediction.

Generate a plot in the current directory (specified by -p .) that provides a summary of the bandwidth-latency of the baseline profiling and the target system prediction. It uses:

• Profiling data for the HPCG application, located at $PROFETPATH/tests/app_profiles/cascade_lake__DDR4_2666__local_socket/HPCG.
• The configuration for the system, located at $PROFETPATH/configs/sample.json.
• Baseline precomputed memory bandwidth-latency curves, located at $PROFETPATH/data/bw_lat_curves/cascade_lake__DDR4_2666__local_socket/.
• Target precomputed memory bandwidth-latency curves, located at $PROFETPATH/data/bw_lat_curves/cascade_lake__DDR4_2666__upi_remote_socket/.

profet -w \
-p . \
-a $PROFETPATH/tests/app_profiles/cascade_lake__DDR4_2666__local_socket/HPCG \
-c $PROFETPATH/configs/sample.json \
--mem-base $PROFETPATH/data/bw_lat_curves/cascade_lake__DDR4_2666__local_socket/ \
--mem-target $PROFETPATH/data/bw_lat_curves/cascade_lake__DDR4_2666__upi_remote_socket/

Benchmarking

Generate two plots in the current directory (specified by -p .) of a benchmark (a collection of application profilings), summarizing the CPI and IPC performance of each application. It uses:

• Profiling data for all the benchmark applications, located at $PROFETPATH/tests/app_profiles/cascade_lake__DDR4_2666__local_socket/.
• The configuration for the system, located at $PROFETPATH/configs/sample.json.
• Baseline precomputed memory bandwidth-latency curves, located at $PROFETPATH/data/bw_lat_curves/cascade_lake__DDR4_2666__local_socket/.
• Target precomputed memory bandwidth-latency curves, located at $PROFETPATH/data/bw_lat_curves/cascade_lake__DDR4_2666__upi_remote_socket/.

profet -w --benchmarking \
-p . \
-a $PROFETPATH/tests/app_profiles/cascade_lake__DDR4_2666__local_socket/ \
-c $PROFETPATH/configs/sample.json \
--mem-base $PROFETPATH/data/bw_lat_curves/cascade_lake__DDR4_2666__local_socket/ \
--mem-target $PROFETPATH/data/bw_lat_curves/cascade_lake__DDR4_2666__upi_remote_socket/

Python Package Usage

To use the profet package in your Python code:

import profet

Documentation: You can find detailed documentation by opening docs/html/index.html in your web browser.

Running Tests

To ensure the functionality and integrity of the software, it's recommended to execute the test suite from the projects root folder.

Command:

python tests/run_tests.py

The complete test suite should conclude in under 5 minutes. Please ensure all tests pass before proceeding with further operations or contributions.

License

PROFET code is released under the BSD-3 License.