diff --git a/README.md b/README.md
index 08d1bfa..756cbd4 100644
--- a/README.md
+++ b/README.md
@@ -52,7 +52,7 @@ Run.py Parameters:
 	    - chembl-1024-jaccard
 	    - molport-1024-jaccard
     algorithm: algorithm name (Required)
-	    Options:
+	    Choices:
 	    - Balltree(Sklearn)
 	    - Bruteforce
 	    - Datasketch
@@ -100,6 +100,67 @@ Command Examples (for Singularity only):
 
     python run.py --dataset=chembl-1024-jaccard --algorithm='Hnsw(Nmslib)' --count=100 --sif-dir="./singularity"  --batch
 
+# Visualization of Execution Results under a PC
+1. Run python plot.py
+
+plot.py Parameters:
+
+    dataset: dataset name (Required)
+	    Examples:
+	    - chembl-1024-jaccard
+	    - molport-1024-jaccard
+    count: the value of K for top-K nearest neighbor search
+	    Default: 10
+	output/-o: the output file
+	x-axis/-x: which metric to use on the X-axis
+	    Choices:
+	    - k-nn: Recall for top-K nearest neighbor search (Default)
+	    - range: Recall for range query
+	    - qps: Queries per second (1/s)
+	    - build: Indexing time (s)
+	    - indexsize: Index size (kB)
+	y-axis/-y: which metric to use on the Y-axis
+	    Choices:
+	    - k-nn: Recall for top-K nearest neighbor search
+	    - range: Recall for range query
+	    - qps: Queries per second (1/s) (Default)
+	    - build: Indexing time (s)
+	    - indexsize: Index size (kB)
+	x-log/-X: Draw the X-axis using a logarithmic scale
+		Default: False
+	y-log/-Y: draw the Y-axis using a logarithmic scale
+		Default: False
+	raw: show raw results (not just Pareto frontier) in faded colours
+		Default: False
+	batch: batch query mode
+		Default: False
+	rq: range query / threshold-based query mode
+		Default: False
+	radius: in the range query mode, the used cut-off value. Here the distance is used, so if all near neighbors with a similarity coefficient larger than 0.8, please set it 0.2.
+		Default: 0.3
+
+
+Command Examples:
+- Plot results on chembl-1024-jaccard dataset for top-K (K=100) nearest neighbor query to "results/chembl-1024-jaccard-100.png". X-axis: recall. Y-axis: qps, log-scale.
+
+    python plot.py --dataset=chembl-1024-jaccard -Y --count=100 -o=results/chembl-1024-jaccard-100
+
+- Plot results on molport-1024-jaccard dataset for top-K (K=10) nearest neighbor query to "results/molport-1024-jaccard-indexsize-10.png". X-axis: recall. Y-axis: index size, log-scale.
+
+    ython plot.py --dataset=molport-1024-jaccard -Y -y=indexsize --count=10 -o=results/molport-1024-jaccard-indexsize-10
+
+- Plot results on molport-1024-jaccard dataset for top-K (K=10) nearest neighbor query to "results/molport-1024-jaccard-buildtime-10.png". X-axis: recall. Y-axis: indexing time, log-scale.
+
+    python plot.py --dataset=molport-1024-jaccard -Y -y=build --count=10 -o=results/molport-1024-jaccard-buildtime-10
+
+- Plot batch mode results on molport-1024-jaccard dataset for top-K (K=100) nearest neighbor query to "results/molport-1024-jaccard-batch-100.png". X-axis: recall. Y-axis: qps, log-scale.
+
+    python plot.py --dataset=molport-1024-jaccard -Y --batch --count=100 -o=results/molport-1024-jaccard-batch-100
+
+- Plot batch mode results on chembl-1024-jaccard dataset for range query with similarity cutoff 0.6 to "results/chembl-1024-jaccard-0_4.png". X-axis: recall (range query). Y-axis: qps, log-scale.
+
+    python plot.py --dataset=chembl-1024-jaccard -Y -x=range --rq --radius=0.4 -o=results/chembl-1024-jaccard-0_4
+
 # Executions under an HPC environment
 
 1. Load anaconda module
@@ -143,6 +204,34 @@ An example "run.sh":
     python run.py --dataset=chembl-1024-jaccard --algorithm='Hnsw(Nmslib)' --count=100 --sif-dir="./singularity"  
 
 
+# Visualization of Execution Results under an HPC environment
+Run your algorithm scripts by SLURM shell
+
+    sbatch plot.sh
+
+An example "plot.sh":
+
+    #!/bin/bash
+    
+    #SBATCH --ntasks=1
+    #SBATCH --exclude=cn[65-69,71-136,325-343,345-353,355-358,360-364,369-398,400-401],gpu[07-10]
+      
+    
+    module load anaconda/5.1.0
+    
+    source activate ann_env
+    
+    module purge
+    
+    module load gcc/5.4.0
+    
+    module load singularity/3.1
+    
+      
+    
+    python plot.py --dataset=chembl-1024-jaccard -Y --count=100 -o=results/chembl-1024-jaccard-100
+
+
 # Parameter tuning
 All algorithmic parameter settings are included in the "./algos.yaml" file.