The MUSE Machine -- an Architecture for Structured Data Flow Computation

Computers employing some degree of data flow organisation are now well established as providing a possible vehicle for concurrent computation. Although data-driven computation frees the architecture from the constraints of the single program counter, processor and global memory, inherent in the classic von Neumann computer, there can still be problems with the unconstrained generation of fresh result tokens if a pure data flow approach is adopted. The advantages of allowing serial processing for those parts of a program which are inherently serial, and of permitting a demand-driven, as well as data-driven, mode of operation are identified and described. The MUSE machine described here is a structured architecture supporting both serial and parallel processing which allows the abstract structure of a program to be mapped onto the machine in a logical way

Dataflow development of medium-grained parallel software

PhD ThesisIn the 1980s, multiple-processor computers (multiprocessors) based on conven- tional processing elements emerged as a popular solution to the continuing demand for ever-greater computing power. These machines offer a general-purpose parallel processing platform on which the size of program units which can be efficiently executed in parallel - the "grain size" - is smaller than that offered by distributed computing environments, though greater than that of some more specialised architectures. However, programming to exploit this medium-grained parallelism remains difficult. Concurrent execution is inherently complex, yet there is a lack of programming tools to support parallel programming activities such as program design, implementation, debugging, performance tuning and so on. In helping to manage complexity in sequential programming, visual tools have often been used to great effect, which suggests one approach towards the goal of making parallel programming less difficult. This thesis examines the possibilities which the dataflow paradigm has to offer as the basis for a set of visual parallel programming tools, and presents a dataflow notation designed as a framework for medium-grained parallel programming. The implementation of this notation as a programming language is discussed, and its suitability for the medium-grained level is examinedScience and Engineering Research Council of Great Britain EC ERASMUS schem

Feladatfüggő felépítésű többprocesszoros célrendszerek szintézis algoritmusainak kutatása = Research of synthesis algorithms for special-purpose multiprocessing systems with task-dependent architecture

Új módszert és egy keretrendszert fejlesztettünk ki olyan speciális többprocesszoros struktúra tervezésére, amely lehetővé teszi a pipeline működtetést akkor is, ha a feladat-leírásban nincs hatékonyan kihasználható párhuzamosság. A szintézis egy magas szintű nyelven (C, Java, stb.) adott feladatleírásból indul ki. Ezután dekompozíciós algoritmus megfelelő szegmenseket képez a program alapján. A szegmensek kívánt száma, a szegmenseket megvalósító processzorok főbb tulajdonságai és a becsült kommunikációs időigények megadhatók bemeneti paraméterekként. Kedvező pipeline felépítés céljából a pipeline adatfolyamok magas szintű szintézisének (HLS) módszertanát alkalmaztuk. Ezek az eszközök az ütemezés és az allokáció révén kísérlik meg az optimalizálást a szegmensekből képzett adatfolyam gráfon. Ezért a kiadódó többprocesszoros felépítés nem egy uniformizált processzor-rács, hanem a megoldandó feladatra formált struktúra, így feladatfüggőnek nevezhető. A módszer modularitása lehetővé teszi a dekompozíciós algoritmusnak és a HLS eszköznek a cseréjét, módosítását az alkalmazási igényektől függően. A módszer kiértékelése céljából olyan HLS eszközt alkalmaztunk, amely a kívánt pipeline újraindítási periódust bemeneti adatként tudja kezelni, és processzorok között egy optimalizált időosztásos, arbitráció-mentes sínrendszert hoz létre. Ebben a struktúrában a kommunikáció szervezéséhez nincs szükség külön szoftver támogatásra, ha a processzorok képesek közvetlen adatforgalomra. | A new method and a framework tool has been developed for designing a special multiprocessing structure for making the pipeline function possible as a special parallel processing, even if there is no efficiently exploitable parallelism in the task description. The synthesis starts from a task description written in a high level language (C, Java, etc). A decomposing algorithm generates proper segments of this program. The desired number of the segments, the main properties of the processor set implementing the segments and the estimated communication time-demand can be given as input parameters. For constructing a pipeline structure, the high-level synthesis (HLS) methodology of pipelined datapaths is applied. These tools attempt to optimize by scheduling and allocating the dataflow graph generated from the segments Thus, the resulted structure is not a uniform processor grid, but it is shaped depending on the task, i.e. it can be called task-dependent. The modularity of the method permits the decomposition algorithm and the HLS tool to be replaced by other ones depending on the requirements of the application. For evaluating the method, a specific HLS tool is applied, which can accept the desired pipeline restart time as input parameter, and generates an optimized time shared simple arbitration-free bus system between the processing units. Therefore, there is no need for extra efforts to organize the communication, if the processing units can transfer data directly

Large-Scale Automatic K-Means Clustering for Heterogeneous Many-Core Supercomputer

Funding: UK EPSRC grants ”Discovery” EP/P020631/1, ”ABC: Adaptive Brokerage for the Cloud” EP/R010528/1.This article presents an automatic k-means clustering solution targeting the Sunway TaihuLight supercomputer. We first introduce a multilevel parallel partition approach that not only partitions by dataflow and centroid, but also by dimension, which unlocks the potential of the hierarchical parallelism in the heterogeneous many-core processor and the system architecture of the supercomputer. The parallel design is able to process large-scale clustering problems with up to 196,608 dimensions and over 160,000 targeting centroids, while maintaining high performance and high scalability. Furthermore, we propose an automatic hyper-parameter determination process for k-means clustering, by automatically generating and executing the clustering tasks with a set of candidate hyper-parameter, and then determining the optimal hyper-parameter using a proposed evaluation method. The proposed auto-clustering solution can not only achieve high performance and scalability for problems with massive high-dimensional data, but also support clustering without sufficient prior knowledge for the number of targeted clusters, which can potentially increase the scope of k-means algorithm to new application areas.PostprintPeer reviewe