From 5dded48ac7ecb0857bb52b0dcbfde7105e5079e2 Mon Sep 17 00:00:00 2001
From: teridax <sven.vogel123@web.de>
Date: Mon, 1 May 2023 15:38:20 +0200
Subject: [PATCH] updated README.md

---
 sparse_vector/README.md | 67 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 67 insertions(+)
 create mode 100644 sparse_vector/README.md

diff --git a/sparse_vector/README.md b/sparse_vector/README.md
new file mode 100644
index 0000000..883e490
--- /dev/null
+++ b/sparse_vector/README.md
@@ -0,0 +1,67 @@
+# Sparse Vector Implementations
+
+This repository aims at comparing various implementations of sparse vectors.
+
+## What is a sparse vector?
+
+A sparse vector is a vector in which most of its elements are zero.
+
+That makes is easier to store because the many zero elements must not be stored.
+
+Though this comes a the cost that we may need to decide between memory saving and computation time.
+
+## Implementations overview
+
+* Hashmap
+* Index Array
+* Compressed Index Array
+* Binary Heap
+
+### Index Array
+
+We can omit all zero elements by storing an index array alongside all non zero values. Each value will be associated with an index in from the index array. This model is only efficient in memory size when the amount of zero elements is at least 50%. Since I used `usize` to store the indices, which is equal to a `u64` in 64-bit architectures, The required memory is:
+
+```
+mem(N) = non_zero_elements * (8 Bytes + 8 Bytes)
+```
+
+One significant downside is the cost of finding each corresponding entry when performing calculations such as the dot product. For this I used a binary search which gives a nive speedup.
+
+### Hashmap Implementation
+
+This implementation uses a hashmap to associate a value with its corresponding index in the vectors column. In Theory this should be as efficient in memory size as the previous array index method.
+
+But in comparision this method requires signifacantly more memory since a hashmap allocates more memory than it can fill in order to reduce collisions.
+
+It has one significant benefit, that being speed in calculations. Looking up values in a hashmap is generally faster than performing a binary seach. Also inserting and deleting is an O(1) operation.
+
+### Compressed Index Array
+
+In order to reduce the size required to store the indices of each value we can compress them by only storing the relative offset to the previous value:
+
+
+| Uncompressed Index | 0 | 7 | 13 | 33 | 45 | 47 | 48 | 57 | ... | 34567 |
+| -------------------- | --- | --- | ---- | ---- | ---- | ---- | ---- | ---- | ----- | ------- |
+| Compressed Index   | 0 | 7 | 6  | 20 | 12 | 2  | 1  | 9  | ... | 23    |
+
+This yields smaller values. Thus we can savely reduce the bandwidth of available bits to store.
+
+In this implementation I reduced to size from 64 to 16 bit. This makes memory usage a lot smaller, but computation gets a lot heavier, since all values have to be decompressed on the fly. A possible improvement would be to cache uncompressed values. May be worth investigating futher.
+
+### Binary Heap
+
+The binary heap has the advantage of being fast with inserting, removing and looking up values.
+We use indices again to sort the values of the vector into to binary heap.
+
+## Comparision
+
+The following values were achieved by using a randomly initialized vector with a length of 10^10 elements from which 2% were non zero. The dot product implementation was single threaded.
+
+
+| Implementation         | Size on Heap (GB) | Runtime of dot product (s) |
+| :----------------------- | ------------------- | ---------------------------- |
+| Naive                  | 80                | N/A                        |
+| Index Array            | 3.6               | 6.254261896                |
+| Hashmap                | 5.4               | 0.732189927                |
+| Compressed Index Array | 2.0               | > 120                      |
+| Binary Heap            | 1.3               | 2.089960966                |