We build this tool mainly for the following issues

#1826
#1828
#1281
#1973

We could also use it in the following cases.

#1973
#2222

gnobench benchmarks the time consumed for each VM CPU OpCode and persistent access to the store, including marshalling and unmarshalling of realm objects.

Design consideration

Minimum Overhead and Footprint

• Constant build flags enable benchmarking.
• Encode operations and measurements in binary.
• Dump to a local file in binary.
• No logging, printout, or network access involved.

Accuracy

• Pause the timer for storage access while performing VM opcode benchmarking.
• Measure each OpCode execution in nanoseconds.
• Store access includes the duration for Amino marshalling and unmarshalling.

It is built on top of @deelawn's design and framework with @jaekwon's input.
#2073

Usage

Simple mode

The benchmark only involves the GnoVM and the persistent store. It benchmarks the bare minimum components, and the results are isolated from other components. We use standardize gno contract to perform the benchmarking.

This mode is the best for benchmarking each major release and/or changes in GnoVM.

make opcode
make storage

Production mode

It benchmarks the node in the production environment with minimum overhead.
We can only benchmark with standardize the contract but also capture the live usage in production environment.
It gives us a complete picture of the node perform.

1. Build the production node with benchmarking flags:

go build -tags "benchmarkingstorage benchmarkingops" gno.land/cmd/gnoland

2. Run the node in the production environment. It will dump benchmark data to a benchmark.bin file.

3. call the realm contracts at gno.land/r/x/benchmark/opcodes and gno.land/r/x/benchmark/storage

4. Stop the server after the benchmarking session is complete.

5. Run the following command to convert the binary dump:

gnobench -bin path_to_benchmark_bin

it converts the binary dump to results.csv and results_stats.csv.

Results ( Examples )

The benchmarking results are stored in two files:

1. The raw results are saved in results.csv.

2. The averages and standard deviations are summarized in results_stats.csv.