Collaborative CPA-ACR-T: PLASMA: Parallel Linear Algebra Software for Multiprocessor Architectures.

等离子体项目为新一代密集线性代数库奠定了基础，这些库通过有效地利用多核系统提供的所有处理器，以尽可能快的速度在多核系统上实现准确的解决方案。为了在这个设计空间内工作并利用百万路并行的力量，等离子体研究人员正在结合新的、高度并行化的算法、可以利用大规模任务并行性的编程和执行模型，以及帮助优化跨一系列不同计算平台的数据局部性的灵活内存管理设施。等离子体的设计空间还受到这样一个事实的制约，即为了支持尽可能广泛的计算科学问题，等离子体库框架必须能够向上和向下扩展，运行在平台开发链的所有级别。等离子体项目专注于以下两个目标：-探索新的、高度并行化的算法：等离子体研究人员已经证明，各种算法(如Cholesky、LU和QR分解)可以用瓦片表示为算法，大大提高了这些操作在多核处理器上的并行性能。他们目前正在将这一概念扩展到广泛的线性代数算法。-开发用于表达并行性的算法描述抽象：基于数据流体系结构中成熟的概念，等离子体研究人员正在开发算法描述的高级抽象，以使任务依赖显式，从而促进多核系统上的调度。由于算法中关于并行性的所有信息都包含在它的依赖图中，该依赖图是一个有向无环图(DAG)，因此等离子方法用基于图的算法定义取代了编程语言的算法定义。

The PLASMA project lays the ground work for a new generation of dense linear algebra libraries that achieve the fastest possible time to an accurate solution on multicore systems by efficiently using all the processors that such systems make available. To work within this design space and leverage the power of million way parallelism, PLASMA researchers are combining new, highly parallelizable algorithms, a programming and execution model that can exploit massive task parallelism, and a flexible memory management facility that helps optimize data locality across a range of different computing platforms. PLASMA's design space is also conditioned by the fact that, in order to support the broadest possible range of computational science problems, the PLASMA library framework must be able to scale both up and down, running at all levels of the platform development chain.The PLASMA project focuses on the following two objectives:- Explore new, highly parallelizable algorithms: PLASMA researchers have shown that a wide range of algorithms (e.g. Cholesky, LU and QR factorizations) can be expressed as algorithms by tiles, greatly improving parallel performance of these operations on multicore processors. They are currently extending this concept to a broad range of linear algebra algorithms. - Develop an algorithm description abstraction for expressing parallelism: Building on the well established concepts from dataflow architectures, PLASMA researchers are developing high-level abstractions for algorithm description that make task dependencies explicit and therefore facilitate scheduling on multicore systems. Since all the information about parallelism in the algorithm is contained in its dependency graph, which is a Direct Acyclic Graph (DAG), the PLASMA approach replaces a programming language algorithm definition with a graph-based algorithm definition.