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<div id="projectname"><a href="https://github.com/cpp-taskflow/cpp-taskflow">Cpp-Taskflow</a>
 <span id="projectnumber">2.2.0</span>
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<div class="title">C0: Project Motivation </div> </div>
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<div class="textblock"><p>Cpp-Taskflow addresses a long-standing problem, <em>how can we make it easier for C++ developers to write efficient parallel programs under complex task dependencies?</em></p>
<h1><a class="anchor" id="TheEraOfMulticore"></a>
The Era of Multicore</h1>
<p>In the past, we embrace <em>free</em> performance scaling on our software thanks to advances in manufacturing technologies and micro-architectural innovations. Approximately for every 1.5 year we can speed up our programs by simply switching to new hardware and compiler vendors that brings 2x more transistors, faster clock rates, and higher instruction-level parallelism. However, this paradigm was challenged by the power wall and increasing difficulties in exploiting instruction-level parallelism. The boost to computing performance has stemmed from changes to multicore chip designs.</p>
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<img src="era_multicore.jpg" alt="era_multicore.jpg" width="50%"/>
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<p>The above sweeping visualization (thanks to Prof. Mark Horowitz and his group) shows the evolution of computer architectures is moving toward multicore designs. Today, multicore processors and multiprocessor systems are common in many electronic products such as mobiles, laptops, desktops, and servers. In order to keep up with the performance scaling, it is becoming necessary for software developers to write parallel programs that utilize the number of available cores.</p>
<h1><a class="anchor" id="LoopLevelParallelism"></a>
Loop-level Parallelism</h1>
<p>The most basic and simplest concept of parallel programming is <em>loop-level parallelism</em>, exploiting parallelism that exists among the iterations of a loop. The program typically partitions a loop of iterations into a set of of blocks, either fixed or dynamic, and run each block in parallel. Below the figure illustrates this pattern.</p>
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<img src="loop-level-parallelism.jpeg" alt="loop-level-parallelism.jpeg" width="30%"/>
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<p>The main advantage of the loop-based approach is its simplicity in speeding up a regular workload in line with Amdahl's Law. Programmers only need to discover independence of each iteration within a loop and, once possible, the parallel decomposition strategy can be easily implemented. Many existing libraries have built-in support to write a parallel-for loop.</p>
<h1><a class="anchor" id="TaskBasedParallelism"></a>
Task-based Parallelism</h1>
<p>The traditional loop-level parallelism is simple but hardly allows users to exploit parallelism in more irregular applications such as graph algorithms, incremental flows, recursion, and dynamically-allocated data structures. To address these challenges, parallel programming and libraries are evolving from the tradition loop-based parallelism to the <em>task-based</em> model.</p>
<div class="image">
<img src="task-level-parallelism.png" alt="task-level-parallelism.png" width="30%"/>
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<p>The above figure shows an example <em>task dependency graph</em>. Each node in the graph represents a task unit at function level and each edge indicates the task dependency between a pair of tasks. Task-based model offers a powerful means to express both regular and irregular parallelism in a top-down manner, and provides transparent scaling to large number of cores. In fact, it has been proven, both by the research community and the evolution of parallel programming standards, task-based approach scales the best with future processor generations and architectures.</p>
<h1><a class="anchor" id="ChallengesOfTaskBasedParallelProgramming"></a>
Challenges of Task-based Parallel Programming</h1>
<p>Parallel programs are notoriously hard to write correctly, regardless of loop-based approach or task-based model. A primary reason is <em>data dependency</em>, some data cannot be accessed until some other data becomes available. This dependency constraint introduces a number of challenges such as data race, thread contention, and consistencies when writing a correct parallel program. Through the evolution of parallel programming standards, it has been proven that the most effective way to overcome these obstacles is a suitable task-based programming model. The programming model affects software developments in various aspects, such as programmability, debugging effort, development costs, efficiencies, etc.</p>
<h1><a class="anchor" id="TheProjectMantra"></a>
The Project Mantra</h1>
<p>The goal of Cpp-Taskflow is simple - <em>We aim to help C++ developers quickly write parallel programs and implement efficient parallel decomposition strategies using the task-based approach</em>. We want developers to write simple and effective parallel code, specifically with the following objectives:</p>
<ul>
<li>Expressiveness </li>
<li>Readability </li>
<li>Transparency</li>
</ul>
<p>In a nutshell, code written with Cpp-Taskflow explains itself. The transparency allows developers to forget about the difficult thread managements at programming time. They can focus on high-level implementation of parallel decomposition algorithms, leaving the concurrency details and scalability handled by Cpp-Taskflow. </p>
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