diff --git a/tutorials/kdd22/Session 3 Approximate Nearest Neighbor Search in PECOS.ipynb b/tutorials/kdd22/Session 3 Approximate Nearest Neighbor Search in PECOS.ipynb
index 21b54aa..89e076b 100644
--- a/tutorials/kdd22/Session 3 Approximate Nearest Neighbor Search in PECOS.ipynb	
+++ b/tutorials/kdd22/Session 3 Approximate Nearest Neighbor Search in PECOS.ipynb	
@@ -1,69 +1,92 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "30e3e801-3659-4455-b405-5a2b083ea952",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "# Approximate Nearest Neighbor (ANN) Search in PECOS "
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e5073aac",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Introduction\n",
+    "Recall that PECOS is a scalable ML library for predictions on enormous and correlated output space. Therefore, we also support inference of embedding-based models such as dual-encoders, which is often formulated as a Maximum Inner Product Search (**MIPS**) or equivalently, an approximate nearest neighbor (**ANN**) search problem.\n",
+    "\n",
+    "In PECOS, we implemented a state-of-the-art graph-based ANN search algorithm, namely **H**ierarchical **N**avigable **S**mall **W**orld (**HNSW**) model. The life-cycle of HNSW can be divided into twp steps:\n",
+    "* *Training*: given user-provided database vectors, build the HNSW graph data structures for indexing;\n",
+    "* *Prediction*: given any query vector, return the top-K approximate nearest vectors indexed in the database.\n",
+    "\n",
+    "More specifically, the **search (i.e., inference)** procedure of HNSW can be summarized as:\n",
+    "* For each layer, conduct best first search traversal. The best candidate serves as an initial point to next layer;\n",
+    "* Traverse from top layer (course-grain graph, long-range link) to bottom layer (fine-grain graph, short-range link);\n",
+    "* The bottom layer graph contains all database items as the nodes.\n",
+    "<div> <br/><img src=\"imgs/pecos-kdd22-tutorial-hnsw-example.png\" width=\"70%\"/> </div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2eaba286-135c-4694-9756-638c8b3b1169",
    "metadata": {},
    "source": [
-    "# Approximate Nearest Neighbor (ANN) Search in PECOS \n",
-    "\n",
-    "PECOS provides the efficient approach for **approximate nearest neighbor (ANN) search**. More specifically, after training an hierarchical navigable small world (HNSW) model (or buildling the **PECOS-HNSW indexer**) with a corpus of vectors, PECOS supports to efficiently infer top-K approximated nearest indexed vectors for an arbitrary query vector. In this part of the tutorial, we will demonstrate how to use PECOS-HNSW tackle the approximate nearest neighbor (ANN) search problem and how to integrate HNSW with PECOS XMR models.\n",
-    "\n",
-    "#### HNSW at a glimpse\n",
-    "The search procedure of HNSW can be summarized as:\n",
-    "* traverse from top layer (course-grain graph, long-range link) to bottom layer (fine-grain graph, short-range link)\n",
-    "* best first search traversal on each graph, where the best candidate serves as initial to next layer\n",
-    "<div> <br/><img src=\"imgs/hnsw_example.png\" width=\\\"80%\\\"/> </div>\n",
-    "\n",
-    "\n",
     "## Highlight of PECOS-HNSW\n",
+    "In this part of tutorial, we introduce the usage of PECOS-HNSW to tackle ANN search problem, and highlight some key functionalities and features in PECOS-HNSW:\n",
     "\n",
-    "* Support both sparse and dense input features\n",
-    "* Support SIMD instructions (SSE, AVX256, and AVX512)\n",
-    "* Modularity implementation\n",
-    "\n",
-    "## Comparison of PECOS and NMSLIB on the sparse data\n",
-    "\n",
-    "#### Disclaimer \n",
-    "The benchmarking results listed in this notebook are based on an `r5dn-24xlarge` AWS instance with 96 Intel(R) Xeon(R) Platinum 8259CL CPUs @ 2.50GHz. With distinct environments, the magnitude of improvments could be also different.\n",
+    "* support inference on both sparse and dense input features;\n",
+    "* support SIMD instructions (AVX, AVX256, and AVX512) and select the best available one in runtime;\n",
+    "* achieve new SOTA results compared to other popular graph-based ANN libraries (e.g., NMSLIB and HNSWLIB).\n",
+    "<div> <br/><img src=\"imgs/pecos-kdd22-tutorial-hnsw-compare.png\" width=\"90%\"/> </div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94b45789-b24f-4ed5-8d9c-0568c2d4d1cc",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Benchmarking PECOS-HNSW with NMSLIB/HNSWLIB\n",
     "\n",
-    "#### Results\n",
-    "* We compare two implementations of HNSW: `PECOS` and `NMSLIB` on a sparse dataset (i.e., RCV1).\n",
-    "* For RCV1, the instances in training/test set are `781,265` and `23,149`, respectively. The feature dimension is `47,236`.\n",
-    "* The HNSW index is constructed under `M=16` and `efConstruction=500`.\n",
-    "* From the table below, we see that, under similar Recall@10, `PECOS` achieves `[88%,93%]` speedup compared to the `NMSLIB` package.\n",
+    "### Disclaimer \n",
+    "We follow [ANN-Benchmark](https://github.com/erikbern/ann-benchmarks) evaluation protocol, which conducts inference on each test query sequentially (**batch_size=1**) with the **single-thread** setup. \n",
+    "The benchmarking results are based on an r5dn-24xlarge (**w/ avx512 supports**) AWS instance with 96 Intel(R) Xeon(R) Platinum 8259CL CPUs @ 2.50GHz. With distinct environments, the magnitude of improvements could be also different.\n",
     "\n",
-    "| M=16, efC=500 |           |                         |    HNSW (PECOS)    |           |                         |    HNSW (NMSLIB)   | speedup (PECOS/NMSLIB) |\n",
-    "|:-------------:|:---------:|:-----------------------:|:------------------:|:---------:|:-----------------------:|:------------------:|:----------------------------:|\n",
-    "|      efS      | Recall@10 | Throughput (#query/sec) | Latency (ms/query) | Recall@10 | Throughput (#query/sec) | Latency (ms/query) |                              |\n",
-    "|            10 |    0.7733 |                5250.297 |             0.1905 |    0.7790 |                2710.256 |             0.3690 |                       93.72% |\n",
-    "|            20 |    0.8545 |                3677.292 |             0.2719 |    0.8581 |                1924.505 |             0.5196 |                       91.08% |\n",
-    "|            40 |    0.9043 |                2409.959 |             0.4149 |    0.9055 |                1271.085 |             0.7867 |                       89.60% |\n",
-    "|            80 |    0.9325 |                1508.349 |             0.6630 |    0.9326 |                 800.999 |             1.2484 |                       88.31% |\n",
-    "|           120 |    0.9434 |                1125.047 |             0.8889 |    0.9426 |                 597.873 |             1.6726 |                       88.17% |\n",
-    "|           200 |    0.9533 |                 763.752 |             1.3093 |    0.9523 |                 404.518 |             2.4721 |                       88.81% |\n",
-    "|           400 |    0.9621 |                 433.872 |             2.3048 |    0.9608 |                 229.553 |             4.3563 |                       89.01% |\n",
-    "|           600 |    0.9657 |                 305.747 |             3.2707 |    0.9644 |                 161.879 |             6.1775 |                       88.87% |\n",
-    "|           800 |    0.9678 |                 237.651 |             4.2078 |    0.9663 |                 124.806 |             8.0124 |                       90.42% |\n",
+    "<div style=\"display:flex\">\n",
+    "     <div style=\"flex:1;padding-right:10px;\">\n",
+    "          <img src=\"imgs/pecos-kdd22-tutorial-hnsw-rcv1.png\" width=\"80%\"/>\n",
+    "     </div>\n",
+    "     <div style=\"flex:1;padding-left:10px;\">\n",
+    "          <img src=\"imgs/pecos-kdd22-tutorial-hnsw-sift.png\" width=\"80%\"/>\n",
+    "     </div>\n",
+    "</div>\n",
     "\n",
-    "## Hands-on Tutorial\n",
+    "### Results on *RCV1-47236-angular*\n",
     "\n",
-    "The life cycle of a PECOS-HNSW model consists of two stages:\n",
+    "* For RCV1, the instances in training/test set are 781,265 and 23,149, respectively. The feature dimension is 47,236.\n",
+    "* PECOS-HNSW achieves an average of **1.9x** speedup compared to the NMSLIB package.\n",
     "\n",
-    "* building the indexer (training)\n",
-    "* inference (testing).\n",
+    "### Results on on *SIFT-128-euclidean*\n",
     "\n",
-    "### Install PECOS through Python PIP"
+    "* For SIFT, the instances in training/test set are 1,000,000 and 10,000, respectively. The feature dimension is 128.\n",
+    "* PECOS-HNSW achieved an average of **1.3x** speedup compared to the HNSWLIB package.\n",
+    "* PECOS-HNSW++ (ongoing work) achieved an average of **3x** speedup compared to the HNSWLIB package."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f6df49a3",
-   "metadata": {},
-   "outputs": [],
+   "cell_type": "markdown",
+   "id": "d74d7466-e379-4378-9992-19f3bdb09ccc",
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "! pip install libpecos"
+    "## Hands-on Tutorial"
    ]
   },
   {
@@ -76,37 +99,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "140a0d24",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--2022-07-15 21:03:07--  https://archive.org/download/pecos-dataset/ann-benchmarks/rcv1-angular-47236.tar.gz\n",
-      "Resolving archive.org (archive.org)... 207.241.224.2\n",
-      "Connecting to archive.org (archive.org)|207.241.224.2|:443... connected.\n",
-      "HTTP request sent, awaiting response... 302 Found\n",
-      "Location: https://ia802308.us.archive.org/21/items/pecos-dataset/ann-benchmarks/rcv1-angular-47236.tar.gz [following]\n",
-      "--2022-07-15 21:03:07--  https://ia802308.us.archive.org/21/items/pecos-dataset/ann-benchmarks/rcv1-angular-47236.tar.gz\n",
-      "Resolving ia802308.us.archive.org (ia802308.us.archive.org)... 207.241.228.48\n",
-      "Connecting to ia802308.us.archive.org (ia802308.us.archive.org)|207.241.228.48|:443... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 317972212 (303M) [application/octet-stream]\n",
-      "Saving to: ‘rcv1-angular-47236.tar.gz’\n",
-      "\n",
-      "100%[======================================>] 317,972,212 11.0MB/s   in 40s    \n",
-      "\n",
-      "2022-07-15 21:03:47 (7.68 MB/s) - ‘rcv1-angular-47236.tar.gz’ saved [317972212/317972212]\n",
-      "\n",
-      "rcv1-angular-47236/\n",
-      "rcv1-angular-47236/X.trn.npz\n",
-      "rcv1-angular-47236/X.tst.npz\n",
-      "rcv1-angular-47236/Y.tst.npy\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "! wget https://archive.org/download/pecos-dataset/ann-benchmarks/rcv1-angular-47236.tar.gz\n",
     "! tar -zxvf ./rcv1-angular-47236.tar.gz"
@@ -114,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "46dc982b",
    "metadata": {},
    "outputs": [
@@ -128,7 +124,9 @@
    ],
    "source": [
     "import numpy as np\n",
+    "import os, time\n",
     "from pecos.utils import smat_util\n",
+    "from pecos.ann.hnsw import HNSW\n",
     "X_trn = smat_util.load_matrix(\"./rcv1-angular-47236/X.trn.npz\").astype(np.float32)\n",
     "X_tst = smat_util.load_matrix(\"./rcv1-angular-47236/X.tst.npz\").astype(np.float32)\n",
     "Y_tst = smat_util.load_matrix(\"./rcv1-angular-47236/Y.tst.npy\")\n",
@@ -144,18 +142,22 @@
    "source": [
     "### Training Indexer\n",
     "\n",
-    "To train a PECOS-HNSW model, training parameters need to be defined in an object of HNSW.TrainParams as the argument train_params. The key parameters of training a PECOS-HNSW model include:\n",
+    "To train a [PECOS-HNSW](https://github.com/amzn/pecos/tree/v0.4.0/pecos/ann/hnsw) model, training parameters need to be defined in an object of [HNSW.TrainParams](https://github.com/amzn/pecos/blob/v0.4.0/pecos/ann/hnsw/model.py#L33) as the argument `train_params`.\n",
+    "\n",
+    "The key parameters of training a [PECOS-HNSW](https://github.com/amzn/pecos/tree/v0.4.0/pecos/ann/hnsw) model include:\n",
     "* `M` (default 32): The maximum number of edges per node for each layer. A larger M leads to a larger model size and greater memory consumption. Higher/lower M are more suitable for high/low dimensional data or the pursue of high/low recall.\n",
     "* `efC` (default 100): The size of the priority queue for best first search in construction. `efC` can be considered as the trade-off between efficiency and accuracy for indexing. A higher `efC` results in longer construction time but better quality of indexing.\n",
     "* `metric_type` (default ip): The distance metric type for ANN search. PECOS-HNSW currently supports Euclidean distance (`l2`); and inner product (`ip`)\n",
     "* `threads` (default -1): The number of threads for training, or -1 to use all available cores.\n",
     "\n",
-    "The parameters for inference can be also decided as the argument pred_params during model construction so that the model can be directly applied for inference without further parameter designation.\n"
+    "Detailed hyper-parameters can be found in the original HNSW paper ([Malkov et al, TPAMI 2018](https://arxiv.org/abs/1603.09320)).\n",
+    "\n",
+    "The parameters for inference can be also decided as the argument `pred_params` during model construction so that the model can be directly applied for inference without further parameter designation.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "553aaf55",
    "metadata": {},
    "outputs": [
@@ -163,14 +165,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "HNSW Indexer | M 32 efC 100 metric ip | time(s) 11.980276823043823\n"
+      "HNSW Indexer | M 32 efC 100 metric ip | time(s) 12.000147581100464\n"
      ]
     }
    ],
    "source": [
-    "import time\n",
-    "from pecos.ann.hnsw import HNSW\n",
-    "\n",
     "M, efC = 32, 100\n",
     "metric = \"ip\"\n",
     "train_params = HNSW.TrainParams(\n",
@@ -196,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "bf7905f3",
    "metadata": {},
    "outputs": [],
@@ -214,36 +213,30 @@
    "source": [
     "### Inference and Evaluation\n",
     "\n",
-    "To conduct inference with a train HNSW model, prediction parameters need to be defined in an object of HNSW.PredParams as the argument pred_params. The key parameters of inference with a PECOS-HNSW model include:\n",
+    "To conduct inference, prediction parameters need to be defined in an object of [HNSW.PredParams](https://github.com/amzn/pecos/blob/v0.4.0/pecos/ann/hnsw/model.py#L51) as the argument `pred_params`.\n",
     "\n",
+    "The key parameters of inference with a PECOS-HNSW model include:\n",
     "* `efS` (default 100): The size of the priority queue for best first search during inference. Similar to efC, efS can be considered as the trade-off between search efficiency and accuracy. A higher efS results in more accurate results with slower speed. efS is required to be greater than topk.\n",
     "* `topk` (default 10): The number of approximate nearest neighbor to be returned. \n",
     "* `threads` (default -1): The number of searchers for parallel inference, -1 to use all available searchers.\n",
     "\n",
-    "The predict function derives the search results based on a query matrix of shape (# of data points for inference, # of dimentions) and `pred_params`, as well as searchers. The argument `ret_csr` (default `true`) decides the format of returned results as:\n",
+    "Users should also construct `searchers` to avoid memory overhead\n",
+    "```\n",
+    "searchers = model.searchers_create(num_searcher=1) # multiple searchers inference multiple queries in parallel\n",
+    "```\n",
     "\n",
-    "* If `ret_csr` is false, the returned results would be two matrices of shape (# of data points, topk), which indicate the topk indices in the training corpus and the corresponding distances for each testing instance.\n",
-    "* If `ret_csr` is true, the returned results would be a  [Compressed Sparse Row (CSR) matrix](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html) of shape (# of data points, # of points in the training corpus). Each row contains sorted topk distance values at the corresponding columns (i.e., indices in training corpus). The data for each row (i.e., `data[indptr[i]:indptr[i + 1]]`) are also sorted by the distance values.\n",
-    "\n"
+    "The predict function derives search results based on a query matrix of shape (# of data points for inference, # of dimensions), `pred_params`, and `searchers`. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "e25e31d4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Prediction Time = 15.7988 seconds.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "pred_params = HNSW.PredParams(efS=100, topk=10)\n",
-    "searchers = model.searchers_create(num_searcher=1)\n",
+    "searchers = model.searchers_create(num_searcher=1) # multiple searchers inference multiple queries in parallel\n",
     "start_time = time.time()\n",
     "indices, distances = model.predict(\n",
     "    X_tst,\n",
@@ -251,21 +244,33 @@
     "    searchers=searchers,\n",
     "    ret_csr=False,\n",
     ")\n",
-    "pred_time = time.time() - start_time\n",
-    "print(f\"Prediction Time = {pred_time:.4f} seconds.\")"
+    "pred_time = time.time() - start_time"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0ce9aefa",
+   "id": "dc06d282-ba6b-4a57-a963-1203fcc87c63",
    "metadata": {},
+   "source": [
+    "The argument `ret_csr` (default `true`) decides the format of returned results as:\n",
+    "\n",
+    "* If `ret_csr` is false, the returned results would be two matrices of shape (# of data points, topk), which indicate the topk indices in the training corpus and the corresponding distances for each testing instance.\n",
+    "* If `ret_csr` is true, the returned results would be a  [Compressed Sparse Row (CSR) matrix](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html) of shape (# of data points, # of points in the training corpus). Each row contains sorted topk distance values at the corresponding columns (i.e., indices in training corpus). The data for each row (i.e., `data[indptr[i]:indptr[i + 1]]`) are also sorted by the distance values."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ce9aefa",
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "### Evaluation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "38401700",
    "metadata": {},
    "outputs": [],
@@ -279,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "b0b6d72a",
    "metadata": {},
    "outputs": [
@@ -287,7 +292,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "HNSW inference | R@10 0.9025 Throughput(q/s) 1465.236 latency(ms/q)   0.6825\n"
+      "HNSW inference | R@10 0.9020 Throughput(q/s) 1478.575 latency(ms/q)   0.6763\n"
      ]
     }
    ],
@@ -301,14 +306,17 @@
   {
    "cell_type": "markdown",
    "id": "75880f0d",
-   "metadata": {},
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
    "source": [
-    "## Recall vs Throughput Trade-off"
+    "## Appendix: Recall vs Throughput Trade-off"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "12bd7fb6",
    "metadata": {},
    "outputs": [],
@@ -318,9 +326,14 @@
     "    M_list = [16]\n",
     "    efC = 500\n",
     "    topk = 10\n",
-    "    efS_list = [10, 20, 40, 80, 120, 200, 400, 600, 800]\n",
+    "    efS_list = [10, 20, 40, 80, 120, 200, 400, 600]\n",
     "    for M in M_list:\n",
-    "        train_params = HNSW.TrainParams(M=M, efC=efC, metric_type=metric, threads=-1)\n",
+    "        train_params = HNSW.TrainParams(\n",
+    "            M=M,\n",
+    "            efC=efC,\n",
+    "            metric_type=metric,\n",
+    "            threads=-1,\n",
+    "        )\n",
     "        start_time = time.time()\n",
     "        model = HNSW.train(X_trn, train_params=train_params, pred_params=None)\n",
     "        print(\"Indexer | M {} efC {} metric {} | train time(s) {}\".format(\n",
@@ -345,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "0b4af0fb",
    "metadata": {},
    "outputs": [
@@ -353,16 +366,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Indexer | M 16 efC 500 metric ip | train time(s) 46.87919640541077\n",
-      "inference | efS  10 R@10 0.7733 Throughput(q/s) 5250.297 latency(ms/q)   0.1905\n",
-      "inference | efS  20 R@10 0.8545 Throughput(q/s) 3677.292 latency(ms/q)   0.2719\n",
-      "inference | efS  40 R@10 0.9043 Throughput(q/s) 2409.959 latency(ms/q)   0.4149\n",
-      "inference | efS  80 R@10 0.9325 Throughput(q/s) 1508.349 latency(ms/q)   0.6630\n",
-      "inference | efS 120 R@10 0.9434 Throughput(q/s) 1125.047 latency(ms/q)   0.8889\n",
-      "inference | efS 200 R@10 0.9533 Throughput(q/s)  763.752 latency(ms/q)   1.3093\n",
-      "inference | efS 400 R@10 0.9621 Throughput(q/s)  433.872 latency(ms/q)   2.3048\n",
-      "inference | efS 600 R@10 0.9657 Throughput(q/s)  305.747 latency(ms/q)   3.2707\n",
-      "inference | efS 800 R@10 0.9678 Throughput(q/s)  237.651 latency(ms/q)   4.2078\n"
+      "Indexer | M 16 efC 500 metric ip | train time(s) 47.05828666687012\n",
+      "inference | efS  10 R@10 0.7737 Throughput(q/s) 5228.054 latency(ms/q)   0.1913\n",
+      "inference | efS  20 R@10 0.8552 Throughput(q/s) 3665.046 latency(ms/q)   0.2728\n",
+      "inference | efS  40 R@10 0.9043 Throughput(q/s) 2406.416 latency(ms/q)   0.4156\n",
+      "inference | efS  80 R@10 0.9320 Throughput(q/s) 1504.624 latency(ms/q)   0.6646\n",
+      "inference | efS 120 R@10 0.9432 Throughput(q/s) 1122.713 latency(ms/q)   0.8907\n",
+      "inference | efS 200 R@10 0.9536 Throughput(q/s)  763.034 latency(ms/q)   1.3106\n",
+      "inference | efS 400 R@10 0.9622 Throughput(q/s)  433.572 latency(ms/q)   2.3064\n",
+      "inference | efS 600 R@10 0.9656 Throughput(q/s)  305.857 latency(ms/q)   3.2695\n"
      ]
     }
    ],
@@ -370,15 +382,362 @@
     "run_pecos(X_trn, X_tst, Y_tst)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ccea74a4-ca0d-4839-b1fa-b42ce86aea82",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "## Appendix: Plot Recall vs Throughput Curve "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "cc70d86f-a4fc-46b6-a079-860db59efc96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_pareto_frontier(Xs, Ys, maxX=True, maxY=True):\n",
+    "    '''Pareto frontier selection process'''\n",
+    "    sorted_list = sorted([[Xs[i], Ys[i]] for i in range(len(Xs))], reverse=maxY)\n",
+    "    pareto_front = [sorted_list[0]]\n",
+    "    for pair in sorted_list[1:]:\n",
+    "        if maxY:\n",
+    "            if pair[1] >= pareto_front[-1][1]:\n",
+    "                pareto_front.append(pair)\n",
+    "        else:\n",
+    "            if pair[1] <= pareto_front[-1][1]:\n",
+    "                pareto_front.append(pair)\n",
+    "    return pareto_front\n",
+    "\n",
+    "def plot_one(\n",
+    "    results_dict,\n",
+    "    xlim, ylim, title,\n",
+    "    FONTSIZE=28):\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    f, axs = plt.subplots(1, 1, figsize=(10,10))\n",
+    "    for algo_name in results_dict.keys():\n",
+    "        algo_dict = results_dict[algo_name]\n",
+    "        pareto_front = get_pareto_frontier(algo_dict[\"recall\"], algo_dict[\"throughput\"])\n",
+    "        Xs_list, Ys_list = zip(*pareto_front)\n",
+    "        axs.plot(\n",
+    "            Xs_list,\n",
+    "            Ys_list,\n",
+    "            label=algo_name,\n",
+    "            ms=7, mew=3, lw=3,\n",
+    "            color=algo_dict[\"color\"],\n",
+    "            linestyle=algo_dict[\"linestyle\"],\n",
+    "            marker=algo_dict[\"marker\"],\n",
+    "        )\n",
+    "    axs.set_xlim([xlim, 1.01])\n",
+    "    axs.set_ylim([0.0, ylim])\n",
+    "    axs.tick_params(axis='both', which='major', labelsize=FONTSIZE-8)\n",
+    "    axs.tick_params(axis='both', which='minor', labelsize=FONTSIZE-8)\n",
+    "    axs.set_ylabel(\"Throughoput (#queries/sec)\", fontsize=FONTSIZE-4)\n",
+    "    axs.set_xlabel(\"Recall10@10\", fontsize=FONTSIZE-4)\n",
+    "    axs.set_title(title, fontsize=FONTSIZE)\n",
+    "    axs.legend(fontsize=18)\n",
+    "    #axs[i, j].set_legend(loc='upper center', bbox_to_anchor=(0.76, 1.01), ncol=1, fancybox=True, shadow=True, fontsize=FONTSIZE-8)\n",
+    "    axs.grid(visible=True, which='major', color='black', linestyle='-')\n",
+    "    axs.grid(visible=True, which='minor', color='gray', linestyle='--')\n",
+    "    axs.minorticks_on()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c7927f8-626a-4ac6-a889-47c8bd023954",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "### plot RCV1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "be44342f-f53a-4266-a865-42d1e4f0cc0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_results_rcv1():\n",
+    "    results_dict = {\n",
+    "        \"PECOS-HNSW\": {\n",
+    "            \"color\": \"blue\",\n",
+    "            \"marker\": \"D\",\n",
+    "            \"linestyle\": \"-\",\n",
+    "            \"recall\": [0.7733, 0.8545, 0.9043, 0.9325, 0.9434, 0.9533, 0.9621, 0.9657, 0.9678],\n",
+    "            \"throughput\": [5250.297, 3677.292, 2409.959, 1508.349, 1125.047, 763.752, 433.872, 305.747, 237.651],\n",
+    "        },\n",
+    "        \"HNSW(NMSLIB)\": {\n",
+    "            \"color\": \"black\",\n",
+    "            \"marker\": \"o\",\n",
+    "            \"linestyle\": \"--\",\n",
+    "            \"recall\": [0.7790, 0.8581, 0.9055, 0.9326, 0.9426, 0.9523, 0.9608, 0.9644, 0.9663],\n",
+    "            \"throughput\": [2710.256, 1924.505, 1271.085, 800.999, 597.873, 404.518, 229.553, 161.879, 124.806],\n",
+    "        }\n",
+    "    }\n",
+    "    return results_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d8466781-720d-4eeb-8762-314f08f3c656",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results_dict = get_results_rcv1()\n",
+    "plot_one(\n",
+    "    results_dict,\n",
+    "    xlim=0.75,\n",
+    "    ylim=6000,\n",
+    "    title=\"Results on RCV1-47236-angular\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97528c4a-962c-44b6-b250-f03b60fe516f",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "### Plot SIFT-128-euclidean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c55a3ee8-dfd6-4bb9-9cb1-66ce33469058",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_results_sift():\n",
+    "    results_dict = {\n",
+    "        \"PECOS-HNSW$^{++}$ (onging work)\": {\n",
+    "            \"color\": \"red\",\n",
+    "            \"marker\": \"X\",\n",
+    "            \"linestyle\": \"-\",\n",
+    "            \"recall\": [\n",
+    "                0.49571,\n",
+    "                0.68714,\n",
+    "                0.82385,\n",
+    "                0.91453,\n",
+    "                0.94811,\n",
+    "                0.97443,\n",
+    "                0.99149,\n",
+    "                0.59568,\n",
+    "                0.79981,\n",
+    "                0.91674,\n",
+    "                0.97189,\n",
+    "                0.98643,\n",
+    "                0.99524,\n",
+    "                0.99869,\n",
+    "                0.64926,\n",
+    "                0.85598,\n",
+    "                0.95698,\n",
+    "                0.99031,\n",
+    "                0.99637,\n",
+    "                0.99872,\n",
+    "                0.99925,\n",
+    "            ],\n",
+    "            \"throughput\": [\n",
+    "                65812.90,\n",
+    "                32797.30,\n",
+    "                24629.80,\n",
+    "                13685.60,\n",
+    "                 9375.55,\n",
+    "                 5805.03,\n",
+    "                 2917.94,\n",
+    "                53613.60,\n",
+    "                34283.30,\n",
+    "                15798.90,\n",
+    "                11082.60,\n",
+    "                 7682.11,\n",
+    "                 4690.11,\n",
+    "                 2398.27,\n",
+    "                46432.80,\n",
+    "                30219.90,\n",
+    "                17348.00,\n",
+    "                 9557.86,\n",
+    "                 6556.19,\n",
+    "                 4058.41,\n",
+    "                 2072.55,\n",
+    "            ],\n",
+    "        },\n",
+    "        \"PECOS-HNSW\": {\n",
+    "            \"color\": \"blue\",\n",
+    "            \"marker\": \"D\",\n",
+    "            \"linestyle\": \"-\",\n",
+    "            \"recall\": [\n",
+    "                0.5640,\n",
+    "                0.7130,\n",
+    "                0.8350,\n",
+    "                0.9187,\n",
+    "                0.9522,\n",
+    "                0.9765,\n",
+    "                0.9922,\n",
+    "                0.7096,\n",
+    "                0.8404,\n",
+    "                0.9281,\n",
+    "                0.9759,\n",
+    "                0.9882,\n",
+    "                0.9958,\n",
+    "                0.9987,\n",
+    "                0.8014,\n",
+    "                0.9083,\n",
+    "                0.9685,\n",
+    "                0.9918,\n",
+    "                0.9964,\n",
+    "                0.9985,\n",
+    "                0.9990,\n",
+    "            ],\n",
+    "            \"throughput\": [\n",
+    "                38457.696,\n",
+    "                23871.502,\n",
+    "                13948.193,\n",
+    "                 7802.484,\n",
+    "                 5451.538,\n",
+    "                 3436.597,\n",
+    "                 1810.481,\n",
+    "                25668.715,\n",
+    "                16076.992,\n",
+    "                 9331.247,\n",
+    "                 5168.389,\n",
+    "                 3611.553,\n",
+    "                 2284.600,\n",
+    "                 1218.401,\n",
+    "                17172.999,\n",
+    "                10774.329,\n",
+    "                 6191.127,\n",
+    "                 3414.756,\n",
+    "                 2394.785,\n",
+    "                 1527.418,\n",
+    "                  831.661,\n",
+    "            ],\n",
+    "        },\n",
+    "        \"HNSW(HNSWLIB-v0.4)\": {\n",
+    "            \"color\": \"gray\",\n",
+    "            \"marker\": \"o\",\n",
+    "            \"linestyle\": \"--\",\n",
+    "            \"recall\": [\n",
+    "                0.5610,\n",
+    "                0.7121,\n",
+    "                0.8340,\n",
+    "                0.9200,\n",
+    "                0.9525,\n",
+    "                0.9774,\n",
+    "                0.9926,\n",
+    "                0.7107,\n",
+    "                0.8397,\n",
+    "                0.9290,\n",
+    "                0.9763,\n",
+    "                0.9887,\n",
+    "                0.9961,\n",
+    "                0.9989,\n",
+    "                0.7997,\n",
+    "                0.9091,\n",
+    "                0.9693,\n",
+    "                0.9922,\n",
+    "                0.9967,\n",
+    "                0.9990,\n",
+    "                0.9992,\n",
+    "            ],\n",
+    "            \"throughput\": [\n",
+    "                29658.555,\n",
+    "                19267.245,\n",
+    "                11726.411,\n",
+    "                 6800.556,\n",
+    "                 4799.454,\n",
+    "                 3042.605,\n",
+    "                 1623.884,\n",
+    "                18731.696,\n",
+    "                12097.183,\n",
+    "                 7304.240,\n",
+    "                 4155.413,\n",
+    "                 2949.101,\n",
+    "                 1901.381,\n",
+    "                 1040.917,\n",
+    "                11694.866,\n",
+    "                 7513.240,\n",
+    "                 4482.334,\n",
+    "                 2546.814,\n",
+    "                 1815.963,\n",
+    "                 1182.672,\n",
+    "                  655.921,\n",
+    "            ],\n",
+    "        }\n",
+    "    }\n",
+    "    return results_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "7b1c7618-b89c-4173-849a-28fe7c27cff1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results_dict = get_results_sift()\n",
+    "plot_one(\n",
+    "    results_dict,\n",
+    "    xlim=0.55,\n",
+    "    ylim=60000,\n",
+    "    title=\"Results on SIFT-128-euclidean\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "149cb520-26dc-40cd-a6e5-328062ca36d6",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
+    "tags": []
+   },
+   "source": [
+    "## Appendix: Install PECOS and NMSLIB"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b58276b2",
    "metadata": {
+    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
-    "## Appendix: Install PECOS and NMSLIB\n",
-    "\n",
     "### Install via Conda \n",
     "```bash\n",
     "conda create -n pecos-hnsw-tutorial python=3.8\n",
@@ -479,14 +838,6 @@
     "    --entrypoint /bin/bash ${DOCKER_IMAGE}\n",
     "```"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b0b85b8a-f1a6-42f0-acf0-6596361e35b3",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -505,7 +856,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.10"
+   "version": "3.9.12"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/kdd22/imgs/hnsw_example.png b/tutorials/kdd22/imgs/hnsw_example.png
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diff --git a/tutorials/kdd22/imgs/pecos-kdd22-tutorial-hnsw-rcv1.png b/tutorials/kdd22/imgs/pecos-kdd22-tutorial-hnsw-rcv1.png
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diff --git a/tutorials/kdd22/imgs/pecos-kdd22-tutorial-hnsw-sift.png b/tutorials/kdd22/imgs/pecos-kdd22-tutorial-hnsw-sift.png
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