vector

💜 A supercharged std::vector implementation (minus Allocator).

This is meant to show you why you should ditch C++ STLs when performance is critical.
lni::vector should always be faster or just as fast as other implementations.

Since the implementation is compliant with the current C++17 Working Draft (minus Allocator),
lni::vector should be a drop-in replacement for std::vector in most cases.

Just note that lni::vector can generate redundancies up to 3x the data size (4x total).
(Consider using shrink_to_fit() to remove redundancies, but beware that a memory reallocation would take place.)

Usage

A very simple sample is provided below.

For details, refer to tester.cpp or an online reference.

#include "vector.hpp"

int main() {
	int i;
	lni::vector<int> v1;
	for (i = 0; i < 10000000; ++i)
		v1.push_back(i);
	for (auto &n: v1)
	 	printf("%d ", n);

	return 0;
}

Test Results

lni::vector is tested with all major compilers (gcc 6, clang 3.8 and VS14).
I've also included some sample test benches and a simple test script.

Current Benches

• back_insertion
• insertion
• array_op
• stack

Bench Usage

cd bench
./test.sh {Bench Name}

Bench Results

back_insertion

---

Hardware: 50GB Vmware Disk, 4GB RAM, 1 vCore (Guest VM, Host: 1TB SSHD, 16GB RAM, i7-3770)
Environment: gcc 6.1.1, Kubuntu 16.04 LTS

* std::vector
0.364s

* lni::vector
0.189s

* folly::fbvector
0.379s

➔ 92 - 100% faster

---

Hardware: 120GB SSD, 16GB RAM, i7-3770 (Desktop)
Environment: Visual Studio 2015, Windows 10 Enterprise 64-bit

* std::vector
0.651s

* lni::vector
0.261s

➔ 149% faster

---

Hardware: 16GB Persistent Disk, 3.75GB RAM, 1 HyperThread on 2.5GHz Xeon E5 v2 (Google Compute Engine n1-standard-1)
Environment: gcc 6.1.1, Ubuntu 16.04 LTS

* std::vector
0.599s

* lni::vector
0.281s

➔ 113% faster

---

Hardware: 16GB Persistent Disk, 3.75GB RAM, 1 HyperThread on 2.5GHz Xeon E5 v2 (Google Compute Engine n1-standard-1)
Environment: clang 3.8.0, Ubuntu 16.04 LTS

* std::vector
0.443s

* lni::vector
0.253s

➔ 75% faster

---

Hardware: 256GB SSD, 16GB RAM, i5-4260U (MacBook Air Early 2014)
Environment: gcc 6.1.0, OS X El Capitan

* std::vector
0.832s

* lni::vector
0.457s

➔ 82% faster

---

Hardware: 256GB SSD, 16GB RAM, i5-4260U (MacBook Air Early 2014)
Environment: clang-703.0.31, OS X El Capitan

* std::vector
0.789s

* lni::vector
0.487s

➔ 62% faster

Discussion

Why is lni::vector faster?

Fact 1. A LOT of people misuse std::vector.

Fact 2. Memory copying is expensive.

Most std::vector implementations are written as Dynamic Table.
The growth factors for these implementations are usually no greater than 2.
Also, the initial reserved sizes are usually assigned only 1.

This means:

Memory reallocations are very likely to take place, especially in the beginning.

• You usually need more than 1 single space
• Low growth factor and low initial reserved size means small sizes in the beginning (1, 2, 4, 8 ...), even though it grows fast later on

Assuming the growth factor is 2, the average cost of inserting a new element is 3, which is reasonably high.

• Use your favorite complexity analysis method: Aggregation, Accounting, Potential ... etc., any will do
• The average cost for lni::vector is 2.333, and is in fact lower for the reason stated below

The impact of growth factors is underestimated.

• In these analyses, we "falsely" assume that a memory reallocation would be the last operation
• The actual average cost is in fact lower, because the costs for the elements inserted after the last memory reallocation are essentially FREE

The correct way to use your vector

If you still want to use STLs,
ALWAYS reserve a reasonable size before you start inserting elements.

A better way in my opinion, is to:
Use an implementation with a high growth factor like mine,
and use shrink_to_fit() remove redundancies after you've completed inserting all the elements.
This is because you don't always know how much space you need (Sometimes it depends on the input),
and over-reserving isn't always a good thing.

License

lni::vector by Jasmine "lnishan" Chen is licensed under a Creative Commons Attribution 4.0 International License.