Add performance evaluation results in README

Author: IlyaMuravjov

README.md

@@ -11,17 +11,42 @@ and work with them in terms of linear algebra.
 For the installation instructions, refer to [docs/install.md](docs/install.md).  
 
 ## CLI
-CFPQ_PyAlgo provides a command line interface for running 
+The CFPQ_PyAlgo project includes a command line interface for running 
 all-pairs CFPQ solver with relation query semantics.
 
 For more details, refer to [docs/cli.md](docs/cli.md).  
 
 ## Evaluation
 
-CFPQ_PyAlgo provides scripts for evaluating performance 
-of various CFPQ solvers (including third-party ones).
+The CFPQ_PyAlgo project includes a [CFPQ evaluator](cfpq_eval) tool for evaluating the performance 
+of various CFPQ solvers.
 
-For more details, refer to [docs/eval.md](docs/eval.md).
+For more details on [CFPQ evaluator](cfpq_eval) usage, refer to [docs/eval.md](docs/eval.md).
+
+We used the [CFPQ evaluator](cfpq_eval) to compare our solver, FastMatrixCFPQ, with five 
+state-of-the-art competitors:
+[PEARL](https://figshare.com/articles/dataset/ASE_2023_artifact/23702271),
+[POCR](https://github.com/kisslune/POCR),
+[KotGLL](https://github.com/vadyushkins/kotgll),
+[Graspan](https://github.com/Graspan/Graspan-C), and
+[Gigascale](https://bitbucket.org/jensdietrich/gigascale-pointsto-oopsla2015/src),
+as well as with the previous version of our solver, MatrixCFPQ. 
+The input data was provided by the
+[CFPQ_Data](https://github.com/FormalLanguageConstrainedPathQuerying/CFPQ_Data),
+[CFPQ_JavaGraphMiner](https://github.com/FormalLanguageConstrainedPathQuerying/CFPQ_JavaGraphMiner), and
+[CPU17-graphs](https://github.com/kisslune/CPU17-graphs)
+projects and is included in the [CFPQ evaluator distribution](docs/eval_install.md). 
+We ran the evaluation five times on a Ryzen 9 7900X CPU with 128 GB of DDR5 RAM, 
+with a timeout of 10,000 seconds for each solver invocation.
+
+As shown in the bar chart below, FastMatrixCFPQ consistently outperforms other universal solvers
+but is sometimes slower than the specialized solver
+[Gigascale](https://bitbucket.org/jensdietrich/gigascale-pointsto-oopsla2015/src),
+which is optimized for one specific context-free grammar.
+Therefore, generalizing the ideas of [Gigascale](https://bitbucket.org/jensdietrich/gigascale-pointsto-oopsla2015/src) (i.e., recognizing and exploiting answer patterns) 
+is one of our future research directions.
+
+![Performance of All-Pairs CFPQ Solvers With Relation Query Semantics](performance.png)
 
 ## Project structure
 The global project structure is the following.

docs/eval.md

@@ -3,7 +3,8 @@
 This document explains how to use `CFPQ_eval` to evaluate the performance of various Context-Free Path Querying (CFPQ)
 solvers, including both the `CFPQ_PyAlgo` solver and third-party tools.
 
-To install the `CFPQ_eval` tool, refer to [eval_install.md](eval_install.md).
+To install the `CFPQ_eval` tool, refer to [eval_install.md](eval_install.md).  
+For our evaluation results, refer to [main project README](../README.md#evaluation).
 
 ## Running the Tool