Team Darth Vector

TEAM, use this for formatting. Wiki Editing Cheat Sheet

Join me, and together we can fork the problem as master and thread

Members

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Alistair Godwin

Giorgi Osadze

Leonel Jara

Generic Programming

Generic Programming is a an objective when writing code to make algorithms reusable and with the least amount of specific code. An example of generic code is STL's templating functions which provide generic code that can be used with many different types without requiring much specific coding for the type( an addition template could be used for int, double, float, short, etc without requiring re-coding). A non-generic library requires types to be specified, meaning more type-specific code has to be created.

STL Background

List of STL Functions:

Algorithms

Are supported by STL for various algorithms such as sorting, searching and accumulation. All can be found within the header "<algorithm>". Examples include sort() and reverse functions()

STL iterators Are supported for serial traversal. Should you use an iterator in parallel, you must be cautious to not change the data while a thread is going through the iterator. They are defined within te header "<iterator>" and is coded as

#include<iterator> 
foo(){
  vector<type> myVector;
  vector<type>::iterator i;
  for( i = myVector.begin(); i < myVector.end(); i++){
   bar();
  }

}

Containers STL supports a variety of containers for data storage. Generally these containers are supported in parallel for read actions, but does not safely support writing to the container with or without reading at the same time.

List of TBB containers:

concurrent_queue : This is the concurrent version of the STL container Queue. This container supports first-in-first-out data storage like its STL counterpart. Multiple threads may simultaneously push and pop elements from the queue. Queue does NOT support and front() or back() in concurrent operations(the front could change while accessing). Also supports iterations, but are slow and are only intended for debug. This is defined within the header "tbb/concurrent_queue.h" and is coded as:

#include <tbb/concurrent_queue.h>
//....//
tbb:concurrent_queue<typename> name; 

concurrent_vector : This is a container class for vectors with concurrent(parallel) support. These vectors do not support insertion or erase operations but support operations done by multiple threads. Note that when elements are inserted, they cannot be removed without calling the clear() member function on it, which removes every element in the array. This is defined within the header "tbb/concurrent_vector.h" and is coded as:

#include <tbb/concurrent_vector.h>
//...//

 tbb:concurrent_vector<typename> name; 

concurrent_hash_map : A container class that supports hashing in parallel. The generated keys are not ordered and there will always be at least 1 element for a key. Defined within "tbb/concurrent_hash_map.h"

quotes: Intel Threaded Building Blocks book. Highly concurrent containers are very important because Standard Template Library (STL) containers generally are not concurrency-friendly, and attempts to modify them concurrently can easily corrupt the containers.

List of TBB Algorithms:

Algorithms

parallel_for

parallel_scan

parallel_reduce

Threads

Lock Convoying Problem

What is a Lock?

A Lock(also called "mutex") is a method for programmers to secure code that when executing in parallel can cause multiple threads to fight for a resource/container for some operation. When threads work in parallel to complete a task with containers, there is no indication when the thread reach the container and need to perform an operation on it. This causes problems when multiple threads are accessing the same place. When doing an insertion on a container with threads, we must ensure only 1 thread is capable of pushing to it or else threads may fight for control. By "Locking" the container, we ensure only 1 thread accesses at any given time.

To use a lock, you program must be working in parallel(ex #include <thread>) and should be completing something in parallel. You can find c++11 locks with #include <mutex>

Code example or Picture here ^_^

#include <iostream>
#include <thread> 
#include <mutex>

//Some threads are spawned which call this function
//Declared the following within the class std::mutex NightsWatch;
void GameOfThronesClass::GuardTheWall(){

//Protect until Unlock() is called. Only 1 thread may do this below at a time. It is //"locked"
NightsWatch.Lock();

//Increment
DaysWithoutWhiteWalkerAttack++;
std::cout << "It has been " << DaysWithoutWhiteWalkerAttack << " since the last attack at Castle Black!\n";

//Allow Next thread to execute the above iteration
NightsWatch.Unlock();


}

Note that there can be problems with locks. If a thread is locked but it is never unlocked, any other threads will be forced to wait which may cause performance issues. Another problem is called "Dead Locking" where each thread may be waiting for another to unlock (and vice versa) and the program is forced to wait and wait .

Parallelism Problems & Convoying in STL

Within STL, issues arise when you attempt to access containers in parallel. With containers, when threads update the container say with push back, it is difficult to determine where the insertion occurred within the container(each thread is updating this container in any order) additionally, the size of the container is unknown as each thread may be updating the size as it goes (thread A may see a size of 4 while thread B a size of 9). For example, in a vector we can push some data to it in parallel but knowing where that data was pushed to requires us to iterate through the vector for the exact location . If we attempt to find the data in parallel with other operations ongoing, 1 thread could search for the data, but another could update the vector size during that time which causes problems with thread 1's search as the memory location may change should the vector need to grow(performs a deep copy to new memory).

Locks can solve the above issue but cause significant performance issues as the threads are forced to wait for each other. This performance hit is known as Lock Convoying.

Performance issues inside STL

Lock Convoying in TBB

TBB attempts to mitigate the performance issue from parallel code when accessing or completing an operation on a container through its own containers such as concurrent_vector. Through concurrent_vector, every time an element is accessed/changed, a return of the index location is given. TBB promises that any time an element is pushed, it will always be in the same location, no matter if the size of the vector changes in memory. With a standard vector, when the size of the vector changes, the data is copied over. If any threads are currently traversing this vector when the size changes, any iterators may no longer be valid.

Study Ref: https://software.intel.com/en-us/blogs/2008/10/20/tbb-containers-vs-stl-performance-in-the-multi-core-age This leads into Concurrent_vector growing below..

Efficiency Comparison Parallel for and concurrent_vector

If only you knew the power of the Building Blocks

Concept: Fine-grained locking

Multiple threads operate on the container by locking only those portions they really need to lock.

Concept: Lock-free algorithms

Bits of knowledge:

STL interfaces are inherently not thread-safe.

Threading Building Blocks containers are not templated with an allocator argument.

Links

http://www.cs.northwestern.edu/~riesbeck/programming/c++/stl-summary.html

http://www.cplusplus.com/reference/stl/

https://www.inf.ed.ac.uk/teaching/courses/ppls/TBBtutorial.pdf

Code Implementation Comparison for STL and TBB

Which library is better depending the on the use case?

One major aspect to look for when parallelizing a piece of code is the Cost- Benefit. Is it worth the time and effort to parallelize a part of your software only to get a small performance gain?

If are just trying to parallelize a section of your code with a simple map, scan, or reduce pattern. Without much thought TBB has you covered. Also, when working with large collections of data TBB with it use of block range coupled with it algorithms makes it simpler to come up with solutions for the collection

TBB helps to lower the cost of smaller performance benefit. Due to TBB requiring less effort to implement.

TBB enables you to specify logical parallelism instead of threads. It eliminates the time needed when developing a backbone for your code when working with threads. For quick and easy solutions for parallelize your code TBB is the way to go.

When trying to fine tune performance, have more control near hardware level, or you need to work near hardware level, the STL library is the way to go. If you are trying to create your own model / deeper threading solution STL gives you the foundations without the needless level of abstraction of other 3rd party’s software.

Implementation Safety for TBB and STL

We are all human and we do make mistakes.

Less mistakes done by developers will equal to less wasted time.

TBB specifically makes it concurrent_vector container not to support insert and erase operations. Only new items can only be pushed back, and cannot be shrunk;

This prevents developers to write bad code. If for example, we would allow insert and erase operations on concurrent_vector. It could cause a big performance hit, burdening both iterating and growing operations. Which will not only make the concurrent containers in TBB unless, but also your program inefficient.

As already stated most of the STL containers are not thread safe. Though some operations in TBB containers are also not thread safe, like reserve() and clear() in concurrent_vector.

Thread Creation, terminating, and synchronizing, partitioning is managed by TBB. This creates a layer of safety on the programmer’s end, has they do not have to deal with the threads themselves, making a developer less prone to make mistakes.

Identifying the worries and responsibility when parallelizing code

The increasing complexity of your code is a natural problem when working in parallel. Knowing the responsibilities as in what you must worry about as a developer is key. When trying quickly implement parallel regions in your code.

STL

Thread Creation, terminating, and synchronizing, partitioning, thread creation, and management must be handled by the programmer.

This increases the work load and the complexity. The thread creation and overall resource for STL is managed by a combination of libraries.

Dividing collection of data is more of the problem when using STL.

C++11 does not have any parallel algorithms. So, any common parallel patterns such as; map, scan, reduce, must be implemented by yourself, or by another library. But the latest STL C++17, will have some parallel algorithms like scan, map, and reduce.

The benefit of STL is due to the fact that you must manage the thread/ resources yourself it give you more control on the code, and fine tuning optimizations. Though this can be a double edge sword and with more control, it will take time implementing the code. Not also mentioning the increase in complexity.

TBB

Thread Creation, terminating, and synchronizing, partitioning, thread creation, and management is managed by TBB. This make you need not to worry about the heavy constructs of threads which are close to the hardware level. Having it close to hardware level makes it less flexible and require writing more need less code. It also has the potential of making your program inefficient if not done correctly.

Like already stated TBB does have Parallel Algorithms support.

The great benefit of TBB is that it is made in such a way that you as a programmer can only worry about one thing. How to parallelize your serial code, and need to not to worry about the resource management.

The TBB model allows you to make quick parallel implementation solutions with less amount of effort. The downside of TBB is since much of the close to hard hardware management is done be hide the scenes, it makes you has a developer have less control on finetuning your program. Unlike how STL allows you to do.

//Leo Note: WIP, maybe will make this look better.