BACKGROUND OF THE INVENTION
1. Field of the Invention
The field of the invention is data processing, or, more specifically, methods, apparatus, and products for qualifying data produced by an application carried out using a plurality of pluggable processing components.
2. Description Of Related Art
The development of the EDVAC computer system of 1948 is often cited as the beginning of the computer era. Since that time, computer systems have evolved into extremely complicated devices. Today's computers are much more sophisticated than early systems such as the EDVAC. Computer systems typically include a combination of hardware and software components, application programs, operating systems, processors, buses, memory, input/output devices, and so on. As advances in semiconductor processing and computer architecture push the performance of the computer higher and higher, more sophisticated computer software has evolved to take advantage of the higher performance of the hardware, resulting in computer systems today that are much more powerful than just a few years ago.
Parallel computing is an area of computer technology that has experienced advances. Parallel computing is the simultaneous execution of the same task (split up and specially adapted) on multiple processors in order to obtain results faster. Parallel computing is based on the fact that the process of solving a problem usually can be divided into smaller tasks, which may be carried out simultaneously with some coordination.
Parallel computers execute applications that include both parallel algorithms and serial algorithms. A parallel algorithm can be split up to be executed a piece at a time on many different processing devices, and then put back together again at the end to get a data processing result. Some algorithms are easy to divide up into pieces. Splitting up the job of checking all of the numbers from one to a hundred thousand to see which are primes could be done, for example, by assigning a subset of the numbers to each available processor, and then putting the list of positive results back together. In this specification, the multiple processing devices that execute the algorithms of an application are referred to as ‘compute nodes.’ A parallel computer is composed of compute nodes and other processing nodes as well, including, for example, input/output (‘I/O’) nodes, and service nodes.
Parallel algorithms are valuable because it is faster to perform some kinds of large computing tasks via a parallel algorithm than it is via a serial (non-parallel) algorithm, because of the way modern processors work. It is far more difficult to construct a computer with a single fast processor than one with many slow processors with the same throughput. There are also certain theoretical limits to the potential speed of serial processors. On the other hand, every parallel algorithm has a serial part and so parallel algorithms have a saturation point. After that point adding more processors does not yield any more throughput but only increases the overhead and cost.
Parallel algorithms are designed also to optimize one more resource-the data communications requirements among the nodes of a parallel computer. There are two ways parallel processors communicate, shared memory or message passing. Shared memory processing needs additional locking for the data and imposes the overhead of additional processor and bus cycles and also serializes some portion of the algorithm.
Message passing processing uses high-speed data communications networks and message buffers, but this communication adds transfer overhead on the data communications networks as well as additional memory need for message buffers and latency in the data communications among nodes. Designs of parallel computers use specially designed data communications links so that the communication overhead will be small but it is the parallel algorithm that decides the volume of the traffic.
Many data communications network architectures are used for message passing among nodes in parallel computers. Compute nodes may be organized in a network as a ‘torus’ or ‘mesh,’ for example. Also, compute nodes may be organized in a network as a tree. A torus network connects the nodes in a three-dimensional mesh with wrap around links. Every node is connected to its six neighbors through this torus network, and each node is addressed by its x,y,z coordinate in the mesh. A torus network lends itself to point to point operations. In a tree network, the nodes typically are connected into a binary tree: each node has a parent, and two children (although some nodes may only have zero children or one child, depending on the hardware configuration). In computers that use a torus and a tree network, the two networks typically are implemented independently of one another, with separate routing circuits, separate physical links, and separate message buffers. A tree network provides high bandwidth and low latency for certain collective operations, message passing operations where all compute nodes participate simultaneously, such as, for example, an allgather.
Many applications that execute in these parallel computing systems are each composed of a plurality of individual, reusable software components. For example, a facial recognition software application may be composed of one reusable software component that performs image preprocessing, another reusable software component that performs face position detection within the processed image, still another reusable software component that measures facial features, and so on.
SUMMARY OF THE INVENTION
Methods, apparatus, and products are disclosed for qualifying data produced by an application carried out using a plurality of pluggable processing components. Qualifying data produced by the application includes: receiving, by an application manager, quality metrics for one of the pluggable processing components; determining, by the application manager, a component quality rating for the pluggable processing component in dependence upon the quality metrics; and assigning, by the application manager, a data quality rating to application data for the application in dependence upon the component quality rating for the pluggable processing component.
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts of exemplary embodiments of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 illustrates an exemplary system for qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention.
FIG. 2 sets forth a block diagram of an exemplary compute node useful in a parallel computer capable of qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention.
FIG. 3A illustrates an exemplary Point To Point Adapter useful in systems capable of qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention.
FIG. 3B illustrates an exemplary Global Combining Network Adapter useful in systems capable of qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention.
FIG. 4 sets forth a line drawing illustrating an exemplary data communications network optimized for point to point operations useful in systems capable of qualifying data produced by an application carried out using a plurality of pluggable processing components in accordance with embodiments of the present invention.
FIG. 5 sets forth a line drawing illustrating an exemplary data communications network optimized for collective operations useful in systems capable of qualifying data produced by an application carried out using a plurality of pluggable processing components in accordance with embodiments of the present invention.
FIG. 6 sets forth a flow chart illustrating an exemplary method for qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention.
FIG. 7 sets forth a flow chart illustrating a further exemplary method for qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention.
FIG. 8 sets forth a flow chart illustrating a further exemplary method for qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention.
DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS
Exemplary methods, apparatus, and computer program products for qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention are described with reference to the accompanying drawings, beginning with FIG. 1. FIG. 1 illustrates an exemplary system for qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention. The system of FIG. 1 includes a parallel computer (100), non-volatile memory for the computer in the form of data storage device (118), an output device for the computer in the form of printer (120), and an input/output device for the computer in the form of computer terminal (122). Parallel computer (100) in the example of FIG. 1 includes a plurality of compute nodes (102).
In the example of FIG. 1, the compute nodes (102) operate to execute an application (200) that is carried out using a plurality of pluggable processing components (210). A pluggable processing component is a software module, specifically a set of computer program instructions, that when executed performs a particular task that is a logical, discrete, reusable building block for more complex software systems. That is, a software developer may create a pluggable processing component to perform a specific task within broader software systems that the software developer can reuse from one system to another. The processing components are referred to as ‘pluggable’ because these components may be plugged together in different ways to form a variety of software applications. For an example, consider a facial recognition software application that is composed of one pluggable processing component that performs image preprocessing, another pluggable processing component that performs face position detection within the processed image, still another pluggable processing component that measures facial features, and so on.
The execution configuration for the pluggable processing components (210) may change during or between periods in which the pluggable processing components (210) are executed on the compute nodes (102). In the example of FIG. 1, each pluggable processing component (210) may be executed on a different compute node (102). In some configurations, however, compute nodes (102) may support multiple pluggable processing components (210). During execution, a service node may move one pluggable processing component (210) from one compute node (102) to another, or multiple pluggable processing components (210) may be collapsed for execution on one compute node (102) from multiple compute nodes (102). The service node may move a pluggable processing component (210) from one node to another by transferring the executable version of the pluggable processing component (210) along with processing state information such as memory contents, cache contents, processor registers, data, and so on from one compute node to another.
The compute nodes (102) are coupled for data communications by several independent data communications networks including a Joint Test Action Group (‘JTAG’) network (104), a global combining network (106) which is optimized for collective operations, and a torus network (108) which is optimized point to point operations. The global combining network (106) is a data communications network that includes data communications links connected to the compute nodes so as to organize the compute nodes as a tree. Each data communications network is implemented with data communications links among the compute nodes (102). The data communications links provide data communications for parallel operations among the compute nodes of the parallel computer. The links between compute nodes are bi-directional links that are typically implemented using two separate directional data communications paths.
In addition, the compute nodes (102) of parallel computer are organized into at least one operational group (132) of compute nodes for collective parallel operations on parallel computer (100). An operational group of compute nodes is the set of compute nodes upon which a collective parallel operation executes. Collective operations are implemented with data communications among the compute nodes of an operational group. Collective operations are those functions that involve all the compute nodes of an operational group. A collective operation is an operation, a message-passing computer program instruction that is executed simultaneously, that is, at approximately the same time, by all the compute nodes in an operational group of compute nodes. Such an operational group may include all the compute nodes in a parallel computer (100) or a subset all the compute nodes. Collective operations are often built around point to point operations. A collective operation requires that all processes on all compute nodes within an operational group call the same collective operation with matching arguments. A ‘broadcast’ is an example of a collective operation for moving data among compute nodes of an operational group. A ‘reduce’ operation is an example of a collective operation that executes arithmetic or logical functions on data distributed among the compute nodes of an operational group. An operational group may be implemented as, for example, an MPI ‘communicator.’
‘MPI’ refers to ‘Message Passing Interface,’ a prior art parallel communications library, a module of computer program instructions for data communications on parallel computers. Examples of prior-art parallel communications libraries that may be improved for use with systems according to embodiments of the present invention include MPI and the ‘Parallel Virtual Machine’ (‘PVM’) library. PVM was developed by the University of Tennessee, The Oak Ridge National Laboratory, and Emory University. MPI is promulgated by the MPI Forum, an open group with representatives from many organizations that define and maintain the MPI standard. MPI at the time of this writing is a de facto standard for communication among compute nodes running a parallel program on a distributed memory parallel computer. This specification sometimes uses MPI terminology for ease of explanation, although the use of MPI as such is not a requirement or limitation of the present invention.
Some collective operations have a single originating or receiving process running on a particular compute node in an operational group. For example, in a ‘broadcast’ collective operation, the process on the compute node that distributes the data to all the other compute nodes is an originating process. In a ‘gather’ operation, for example, the process on the compute node that received all the data from the other compute nodes is a receiving process. The compute node on which such an originating or receiving process runs is referred to as a logical root.
Most collective operations are variations or combinations of four basic operations: broadcast, gather, scatter, and reduce. The interfaces for these collective operations are defined in the MPI standards promulgated by the MPI Forum. Algorithms for executing collective operations, however, are not defined in the MPI standards. In a broadcast operation, all processes specify the same root process, whose buffer contents will be sent. Processes other than the root specify receive buffers. After the operation, all buffers contain the message from the root process.
In a scatter operation, the logical root divides data on the root into segments and distributes a different segment to each compute node in the operational group. In scatter operation, all processes typically specify the same receive count. The send arguments are only significant to the root process, whose buffer actually contains sendcount*N elements of a given data type, where N is the number of processes in the given group of compute nodes. The send buffer is divided and dispersed to all processes (including the process on the logical root). Each compute node is assigned a sequential identifier termed a ‘rank.’ After the operation, the root has sent sendcount data elements to each process in increasing rank order. Rank 0 receives the first sendcount data elements from the send buffer. Rank 1 receives the second sendcount data elements from the send buffer, and so on.
A gather operation is a many-to-one collective operation that is a complete reverse of the description of the scatter operation. That is, a gather is a many-to-one collective operation in which elements of a datatype are gathered from the ranked compute nodes into a receive buffer in a root node.
A reduce operation is also a many-to-one collective operation that includes an arithmetic or logical function performed on two data elements. All processes specify the same ‘count’ and the same arithmetic or logical function. After the reduction, all processes have sent count data elements from computer node send buffers to the root process. In a reduction operation, data elements from corresponding send buffer locations are combined pair-wise by arithmetic or logical operations to yield a single corresponding element in the root process's receive buffer. Application specific reduction operations can be defined at runtime. Parallel communications libraries may support predefined operations. MPI, for example, provides the following pre- defined reduction operations:
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MPI_MAX
maximum
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MPI_MIN
minimum
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MPI_SUM
sum
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MPI_PROD
product
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MPI_LAND
logical and
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MPI_BAND
bitwise and
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MPI_LOR
logical or
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MPI_BOR
bitwise or
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MPI_LXOR
logical exclusive or
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MPI_BXOR
bitwise exclusive or
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In addition to compute nodes, the parallel computer (100) includes input/output (‘I/O’) nodes (110, 114) coupled to compute nodes (102) through the global combining network (106). The compute nodes in the parallel computer (100) are partitioned into processing sets such that each compute node in a processing set is connected for data communications to the same I/O node. Each processing set, therefore, is composed of one I/O node and a subset of compute nodes (102). The ratio between the number of compute nodes to the number of I/O nodes in the entire system typically depends on the hardware configuration for the parallel computer. For example, in some configurations, each processing set may be composed of eight compute nodes and one I/O node. In some other configurations, each processing set may be composed of sixty-four compute nodes and one I/O node. Such example are for explanation only, however, and not for limitation. Each I/O nodes provide I/O services between compute nodes (102) of its processing set and a set of I/O devices. In the example of FIG. 1, the I/O nodes (110, 114) are connected for data communications I/O devices (118, 120, 122) through local area network (‘LAN’) (130) implemented using high-speed Ethernet.
The parallel computer (100) of FIG. 1 also includes a service node (116) coupled to the compute nodes through one of the networks (104). Service node (116) provides services common to pluralities of compute nodes, administering the configuration of compute nodes, loading programs into the compute nodes, starting program execution on the compute nodes, retrieving results of program operations on the computer nodes, and so on. Service node (116) runs a service application (124) and communicates with users (128) through a service application interface (126) that runs on computer terminal (122).
In the example of FIG. 1, the service node (116) has installed upon it an application manager (125). The application manager (125) of FIG. 1 includes a set of computer program instructions capable of qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention. The application manager (125) operates generally for qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention by: receiving quality metrics for one of the pluggable processing components (210); determining a component quality rating for the pluggable processing component (210) in dependence upon the quality metrics; and assigning a data quality rating to application data for the application (200) in dependence upon the quality rating for the pluggable processing component (210).
A quality metric for a pluggable processing component is an indicator used to infer a particular level of quality related to the processing provided by the pluggable processing component or the data processed by that component. A quality metric may track the processing of predefined portions of a pluggable processing component such as, for example, portions of the pluggable processing component known by a software developer to produce low quality data or portion that are known to be error-prone. Consider the exemplary facial recognition system mentioned above carried out by a plurality of pluggable processing components. One of the component may perform measurements of facial features using a variety of algorithms—some algorithms more accurate than others. A quality metric may be implemented as a value for a flag indicating that one of those least accurate measurement algorithms were used to process data.
A quality metric may also track processing of predetermined assert statements of the pluggable processing component. An assert statement is a set of instructions that allows application developers to test assumptions about a pluggable processing component. For example, because all particles travel at a speed less than the speed of light, if a software developer uses a pluggable component to calculate the speed of a particle, the application development might test an assertion that the calculated speed of the particle is less than the speed of light to increase confidence that the component is operating correctly. Each assertion statement generally contains a Boolean expression that application developer believes will be true when the assertion executes. If the Boolean expression is not true, an error occurs. By verifying that the Boolean expression is indeed true, the assertion confirms the application developer's assumptions about the behavior of the pluggable processing component, thereby increasing confidence that the component is free of errors. Accordingly, quality metrics that track such predetermined assert statements may be implemented as a value specifying the number of times that the Boolean expression of a particular assert statement evaluates to false.
A quality metric may also track exceptions or exception types related to a pluggable processing component. An exception is a programming construct or computer hardware mechanism designed to handle the occurrence of some condition that alters the normal flow of execution. One of the most common types of exceptions is an exception that handles the occurrence of errors in a pluggable processing component. A quality metric that tracks exceptions or exception types may be implemented as value specifying the number of times that a particular exception or type of exception was invoked during the processing of a pluggable processing component.
Still further, a quality metric may track missing portions of data processed by the pluggable processing component or erroneous portions of data processed by the pluggable processing component. Missing or erroneous portions of data for a pluggable processing component may be detected by comparing the data processed by a particular pluggable processing component to a data profile. The data profile may specify the type of data that the pluggable processing component expects to process at various stages of execution. The data profile may be generated by the application developer or based on historical execution information. For an example of a quality metric that tracks erroneous or missing data, consider again a facial recognition system carried out using a plurality of pluggable processing components. Also consider again the measurement component that measures facial features. Further consider that the application developer created a data profile for the measurement component specifying that the input data should include information regarding a person's nose, eyes, ears, and chin in various vectors of particular sizes and value ranges. By comparing the input data provided to the measurement component with the data profile, the measurement component may determine whether data is missing such as, for example, whether any nose data is provided. By comparing the input data provided to the measurement component with the data profile, the measurement component may also determine whether the input data is erroneous such as, for example, whether the vector containing nose information is the wrong size or has values out of the specified ranges.
Readers will note that the quality metrics described above are for explanation and not for limitation. In fact, other quality metrics as will occur to those of skill in the art may also be useful in qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention.
In the example of FIG. 1, the plurality of compute nodes (102) are implemented in a parallel computer (100) and are connected together using a plurality of data communications networks (104, 106, 108). The point to point network (108) is optimized for point to point operations. The global combining network (106) is optimized for collective operations. Although qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention is described above in terms of an architecture for a parallel computer, readers will note that such an embodiment is for explanation only and not for limitation. In fact, qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention may be implemented using a variety of computer system architectures composed of a plurality of nodes network-connected together, including for example architectures for a cluster of nodes, a distributed computing system, a grid computing system, and so on.
The arrangement of nodes, networks, and I/O devices making up the exemplary system illustrated in FIG. 1 are for explanation only, not for limitation of the present invention. Data processing systems capable of qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention may include additional nodes, networks, devices, and architectures, not shown in FIG. 1, as will occur to those of skill in the art. Although the parallel computer (100) in the example of FIG. 1 includes sixteen compute nodes (102), readers will note that parallel computers capable of qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention may include any number of compute nodes. In addition to Ethernet and JTAG, networks in such data processing systems may support many data communications protocols including for example TCP (Transmission Control Protocol), IP (Internet Protocol), and others as will occur to those of skill in the art. Various embodiments of the present invention may be implemented on a variety of hardware platforms in addition to those illustrated in FIG. 1.
Qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention may be generally implemented on a parallel computer that includes a plurality of compute nodes, among other types of exemplary systems. In fact, such computers may include thousands of such compute nodes. Each compute node is in turn itself a kind of computer composed of one or more computer processors, its own computer memory, and its own input/output adapters. For further explanation, therefore, FIG. 2 sets forth a block diagram of an exemplary compute node (152) useful in a parallel computer capable of qualifying data produced by an application carried out using a plurality of pluggable processing components (210) according to embodiments of the present invention. The compute node (152) of FIG. 2 includes one or more computer processors (164) as well as random access memory (‘RAM’) (156). The processors (164) are connected to RAM (156) through a high-speed memory bus (154) and through a bus adapter (194) and an extension bus (168) to other components of the compute node (152).
Stored in RAM (156) of FIG. 2 are one or more pluggable processing components (210). The pluggable processing components (210) of FIG. 2 are combined together to carry out a particular application. As mentioned above, a pluggable processing component is a set of computer program instructions that when executed performs a particular task that is a logical, discrete, reusable building block for more complex software systems.
Also stored in RAM (156) of FIG. 2 is an application manager (125). The application manager (125) of FIG. 2 includes a set of computer program instructions capable of qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention. The application manager (125) operates generally for qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention by: receiving quality metrics for one of the pluggable processing components (210); determining a component quality rating for the pluggable processing component (210) in dependence upon the quality metrics; and assigning a data quality rating to application data for the application (200) in dependence upon the quality rating for the pluggable processing component (210).
Also stored RAM (156) is a messaging module (161), a library of computer program instructions that carry out parallel communications among compute nodes, including point to point operations as well as collective operations. User-level applications such as pluggable processing components (210) effect data communications with other applications running on other compute nodes by calling software routines in the messaging modules (161). A library of parallel communications routines may be developed from scratch for use in systems according to embodiments of the present invention, using a traditional programming language such as the C programming language, and using traditional programming methods to write parallel communications routines. Alternatively, existing prior art libraries may be used such as, for example, the ‘Message Passing Interface’ (‘MPI’) library, the ‘Parallel Virtual Machine’ (‘PVM’) library, and the Aggregate Remote Memory Copy Interface (‘ARMCI’) library.
Also stored in RAM (156) is an operating system (162), a module of computer program instructions and routines for an application program's access to other resources of the compute node. It is typical for an application program and parallel communications library in a compute node of a parallel computer to run a single thread of execution with no user login and no security issues because the thread is entitled to complete access to all resources of the node. The quantity and complexity of tasks to be performed by an operating system on a compute node in a parallel computer therefore are smaller and less complex than those of an operating system on a serial computer with many threads running simultaneously. In addition, there is no video I/O on the compute node (152) of FIG. 2, another factor that decreases the demands on the operating system. The operating system may therefore be quite lightweight by comparison with operating systems of general purpose computers, a pared down version as it were, or an operating system developed specifically for operations on a particular parallel computer. Operating systems that may usefully be improved, simplified, for use in a compute node include UNIX™, Linux™, Microsoft Vista™, AIX™, IBM's i5/OS™, and others as will occur to those of skill in the art.
The exemplary compute node (152) of FIG. 2 includes several communications adapters (172, 176, 180, 188) for implementing data communications with other nodes of a parallel computer. Such data communications may be carried out serially through RS-232 connections, through external buses such as USB, through data communications networks such as IP networks, and in other ways as will occur to those of skill in the art. Communications adapters implement the hardware level of data communications through which one computer sends data communications to another computer, directly or through a network. Examples of communications adapters useful in systems for qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention include modems for wired communications, Ethernet (IEEE 802.3) adapters for wired network communications, and 802.11b adapters for wireless network communications.
The data communications adapters in the example of FIG. 2 include a Gigabit Ethernet adapter (172) that couples example compute node (152) for data communications to a Gigabit Ethernet (174). Gigabit Ethernet is a network transmission standard, defined in the IEEE 802.3 standard, that provides a data rate of 1 billion bits per second (one gigabit). Gigabit Ethernet is a variant of Ethernet that operates over multimode fiber optic cable, single mode fiber optic cable, or unshielded twisted pair.
The data communications adapters in the example of FIG. 2 includes a JTAG Slave circuit (176) that couples example compute node (152) for data communications to a JTAG Master circuit (178). JTAG is the usual name used for the IEEE 1149.1 standard entitled Standard Test Access Port and Boundary-Scan Architecture for test access ports used for testing printed circuit boards using boundary scan. JTAG is so widely adapted that, at this time, boundary scan is more or less synonymous with JTAG. JTAG is used not only for printed circuit boards, but also for conducting boundary scans of integrated circuits, and is also useful as a mechanism for debugging embedded systems, providing a convenient “back door” into the system. The example compute node of FIG. 2 may be all three of these: It typically includes one or more integrated circuits installed on a printed circuit board and may be implemented as an embedded system having its own processor, its own memory, and its own I/O capability. JTAG boundary scans through JTAG Slave (176) may efficiently configure processor registers and memory in compute node (152) for use in qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention.
The data communications adapters in the example of FIG. 2 includes a Point To Point Adapter (180) that couples example compute node (152) for data communications to a network (108) that is optimal for point to point message passing operations such as, for example, a network configured as a three-dimensional torus or mesh. Point To Point Adapter (180) provides data communications in six directions on three communications axes, x, y, and z, through six bidirectional links: +x (181), −x (182), +y (183), −y (184), +z (185), and −z (186).
The data communications adapters in the example of FIG. 2 includes a Global Combining Network Adapter (188) that couples example compute node (152) for data communications to a network (106) that is optimal for collective message passing operations on a global combining network configured, for example, as a binary tree. The Global Combining Network Adapter (188) provides data communications through three bidirectional links: two to children nodes (190) and one to a parent node (192).
Example compute node (152) includes two arithmetic logic units (‘ALUs’). ALU (166) is a component of processor (164), and a separate ALU (170) is dedicated to the exclusive use of Global Combining Network Adapter (188) for use in performing the arithmetic and logical functions of reduction operations. Computer program instructions of a reduction routine in parallel communications library (160) may latch an instruction for an arithmetic or logical function into instruction register (169). When the arithmetic or logical function of a reduction operation is a ‘sum’ or a ‘logical or,’ for example, Global Combining Network Adapter (188) may execute the arithmetic or logical operation by use of ALU (166) in processor (164) or, typically much faster, by use dedicated ALU (170).
The example compute node (152) of FIG. 2 includes a direct memory access (‘DMA’) controller (195), which is computer hardware for direct memory access and a DMA engine (195), which is computer software for direct memory access. Direct memory access includes reading and writing to memory of compute nodes with reduced operational burden on the central processing units (164). A DMA transfer essentially copies a block of memory from one compute node to another. While the CPU may initiates the DMA transfer, the CPU does not execute it. In the example of FIG. 2, the DMA engine (195) and the DMA controller (195) support the messaging module (161).
For further explanation, FIG. 3A illustrates an exemplary Point To Point Adapter (180) useful in systems capable of qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention. Point To Point Adapter (180) is designed for use in a data communications network optimized for point to point operations, a network that organizes compute nodes in a three-dimensional torus or mesh. Point To Point Adapter (180) in the example of FIG. 3A provides data communication along an x-axis through four unidirectional data communications links, to and from the next node in the −x direction (182) and to and from the next node in the +x direction (181).
Point To Point Adapter (180) also provides data communication along a y-axis through four unidirectional data communications links, to and from the next node in the −y direction (184) and to and from the next node in the +y direction (183). Point To Point Adapter (180) in FIG. 3A also provides data communication along a z-axis through four unidirectional data communications links, to and from the next node in the −z direction (186) and to and from the next node in the +z direction (185).
For further explanation, FIG. 3B illustrates an exemplary Global Combining Network Adapter (188) useful in systems capable of qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention. Global Combining Network Adapter (188) is designed for use in a network optimized for collective operations, a network that organizes compute nodes of a parallel computer in a binary tree. Global Combining Network Adapter (188) in the example of FIG. 3B provides data communication to and from two children nodes through four unidirectional data communications links (190). Global Combining Network Adapter (188) also provides data communication to and from a parent node through two unidirectional data communications links (192).
For further explanation, FIG. 4 sets forth a line drawing illustrating an exemplary data communications network (108) optimized for point to point operations useful in systems capable of qualifying data produced by an application carried out using a plurality of pluggable processing components in accordance with embodiments of the present invention. In the example of FIG. 4, dots represent compute nodes (102) of a parallel computer, and the dotted lines between the dots represent data communications links (103) between compute nodes. The data communications links are implemented with point to point data communications adapters similar to the one illustrated for example in FIG. 3A, with data communications links on three axes, x, y, and z, and to and fro in six directions +x (181), −x (182), +y (183), −y (184), +z (185), and −z (186). The links and compute nodes are organized by this data communications network optimized for point to point operations into a three dimensional mesh (105). The mesh (105) has wrap-around links on each axis that connect the outermost compute nodes in the mesh (105) on opposite sides of the mesh (105). These wrap-around links form part of a torus (107). Each compute node in the torus has a location in the torus that is uniquely specified by a set of x, y, z coordinates. Readers will note that the wrap-around links in the y and z directions have been omitted for clarity, but are configured in a similar manner to the wrap-around link illustrated in the x direction. For clarity of explanation, the data communications network of FIG. 4 is illustrated with only 27 compute nodes, but readers will recognize that a data communications network optimized for point to point operations for use in qualifying data produced by an application carried out using a plurality of pluggable processing components in accordance with embodiments of the present invention may contain only a few compute nodes or may contain thousands of compute nodes.
For further explanation, FIG. 5 sets forth a line drawing illustrating an exemplary data communications network (106) optimized for collective operations useful in systems capable of qualifying data produced by an application carried out using a plurality of pluggable processing components in accordance with embodiments of the present invention. The example data communications network of FIG. 5 includes data communications links connected to the compute nodes so as to organize the compute nodes as a tree. In the example of FIG. 5, dots represent compute nodes (102) of a parallel computer, and the dotted lines (103) between the dots represent data communications links between compute nodes. The data communications links are implemented with global combining network adapters similar to the one illustrated for example in FIG. 3B, with each node typically providing data communications to and from two children nodes and data communications to and from a parent node, with some exceptions. Nodes in a binary tree (106) may be characterized as a physical root node (202), branch nodes (204), and leaf nodes (206). The root node (202) has two children but no parent. The leaf nodes (206) each has a parent, but leaf nodes have no children. The branch nodes (204) each has both a parent and two children. The links and compute nodes are thereby organized by this data communications network optimized for collective operations into a binary tree (106). For clarity of explanation, the data communications network of FIG. 5 is illustrated with only 31 compute nodes, but readers will recognize that a data communications network optimized for collective operations for use in systems for qualifying data produced by an application carried out using a plurality of pluggable processing components in accordance with embodiments of the present invention may contain only a few compute nodes or may contain thousands of compute nodes.
In the example of FIG. 5, each node in the tree is assigned a unit identifier referred to as a ‘rank’ (250). A node's rank uniquely identifies the node's location in the tree network for use in both point to point and collective operations in the tree network. The ranks in this example are assigned as integers beginning with 0 assigned to the root node (202), 1 assigned to the first node in the second layer of the tree, 2 assigned to the second node in the second layer of the tree, 3 assigned to the first node in the third layer of the tree, 4 assigned to the second node in the third layer of the tree, and so on. For ease of illustration, only the ranks of the first three layers of the tree are shown here, but all compute nodes in the tree network are assigned a unique rank.
For further explanation, FIG. 6 sets forth a flow chart illustrating an exemplary method for qualifying data produced by an application (200) carried out using a plurality of pluggable processing components (210) according to embodiments of the present invention. Qualifying data produced by an application (200) according to embodiments of the present invention may be carried out by an application manager installed on a service node such as, for example, a service node as described above. The pluggable processing components (210) of FIG. 6 are executed on a plurality of compute nodes such as, for example, the compute nodes discussed above.
In the example of FIG. 6, the pluggable processing components (210) carry out an application for performing facial recognition of one or more faces in an image. The pluggable processing components (210) illustrated in FIG. 6 carry out facial recognition as follows: An image selection component receives various images as application input and selects a particular image for performing facial recognition. The image selection component provides the selected image to a preprocessing component, which cleans up the image by removing visual noise attributable to the camera capturing the image or other visual noise or aberrations. The preprocessing component provides the preprocessed image to a face detection component that identifies a person's face within the image. The face detection component in turn provides the image and the location of the face in the image to an alignment component that determines the head's position, size, and pose. The alignment component then provides the image and the alignment data to a measurement component that measures the curves of the face on a sub-millimeter or microwave scale and creates a template that describes the features of the face in the image. A representation component receives the template from the measure component and translates the template into a set of codes that represent the features of the face in the image. The representation component then provides the set of codes to a matching component that compares the set of codes with codes representing faces of known persons in a database to identify a match. When performing identity verification, a candidate verification/identification component receives an identifier for a matching face in the database and compares information associated with the matched face in the database with information provided by the person whose face is captured for facial recognition. When performing identification, the candidate verification/identification component receives an identifier for a matching face in the database and provides system administrators with the information associated with the matched face in the database. The candidate verification/identification component then provides the verification/identification information as application output.
The method of FIG. 6 includes receiving (600), by an application manager, quality metrics (602) for one of the pluggable processing components (210). As mentioned above, a quality metric (602) for a pluggable processing component is an indicator used to infer a particular level of quality related to the processing provided by the pluggable processing component or the data processed by that component. The quality metrics (602) of FIG. 6 may track processing of predefined portions of the pluggable processing component or processing of predetermined assert statements of the pluggable processing component. The quality metrics (602) may track exceptions or exception types related to the pluggable processing component. The quality metrics (602) may track missing portions or erroneous portions of data processed by the pluggable processing component. The application manager may receive (600) the quality metrics (602) for one of the pluggable processing components (210) according to the method of FIG. 6 by receiving messages from the pluggable processing component (210) that include the quality metrics (602). An application developer may instrument the computer program instructions for the pluggable processing component (210) to measure the various aspects of processing mentioned above and then transmit those measurements to the application manager. In the example of FIG. 6, the application manager receives quality metrics for the face detection component of the facial recognition application.
The method of FIG. 6 also includes determining (604), by the application manager, a component quality rating (606) for the pluggable processing component in dependence upon the quality metrics (602). The component quality rating (606) of FIG. 6 is an indictor of the overall processing quality provided by a particular pluggable processing component based in part on the quality metrics (602) received from that particular pluggable processing component. The component quality rating (606) of FIG. 6 may be implemented as a numeric value along a particular scale such as, for example, a value of ‘3’ on a scale of ‘1’ to ‘5’ where ‘1’ corresponds with the lowest quality and ‘5’ corresponds with the highest quality. The component quality rating (606) of FIG. 6 may also be implemented using text such that a value of ‘low’ corresponds with low quality, ‘medium’ corresponds with medium quality, ‘high’ corresponds with high quality. The application manager may determine (604) the component quality rating (606) for the pluggable processing component according to the method of FIG. 6 by translating the quality metrics (602) into a component quality rating (606) using a component quality translation ruleset. The translation ruleset associates various component quality rating values with different combinations of values for the quality metrics. For example, consider that the face detection component uses a particular algorithm for detecting a face in a hazy image that is more accurate than the other available algorithms, but is still only accurate sixty-five percent of the time. A translation ruleset may specify that the processing quality of the face detection component is ‘low’ when the quality metrics received from the face detection component indicate that this particular algorithm is used by the face detection component to process data.
The method of FIG. 6 also includes assigning (608), by the application manager, a data quality rating (612) to application data for the application (200) in dependence upon the component quality rating (606) for the pluggable processing component (210). The data quality rating (612) of FIG. 6 is an indicator of the quality of a set of application data resulting from the processing of one or more pluggable processing components of the application (200). The application data may be the output or input of a particular pluggable processing component or a group of combined pluggable processing components. The application manager may assign (608) a data quality rating (612) to application data for the application (200) according to the method of FIG. 6 by translating the component quality rating (606) into a data quality rating using a data quality translation ruleset. Such a translation ruleset associates various data quality ratings with various combinations of component quality ratings for one or more pluggable processing components (210). The data quality translation ruleset may weight the component quality rating (606) for some pluggable component (210) more heavily than others or the component quality ratings (606) for all pluggable components (210) may be given the same weight in deriving a data quality rating (612) based on data processing provided by multiple components.
In the method of FIG. 6, the application manager assigns (608) a data quality rating (612) to application data for the application (200) by publishing (610) the data quality rating (612) for the application data. The application manager may publish (610) the data quality rating (612) for the application data according to the method of FIG. 6 by printing a report with the data quality such as, for example, the data quality report illustrated in FIG. 6. The application manager may publish (610) the data quality rating (612) for the application data according to the method of FIG. 6 by sending a message to a user that contains the data quality rating (612) such as, for example, an email or instant message. Still further, the application manager may publish (610) the data quality rating (612) for the application data according to the method of FIG. 6 by posting the data quality rating (612) to a data quality register for analysis or use by other user or processes. Other ways of publishing (610) the data quality rating (612) for the application data as will occur to those of skill in the art are also well within the scope of the present invention.
The explanation above with reference to FIG. 6 describes an application manager in which assigning a data quality rating to application data for the application includes publishing the data quality rating for the application data. In some other embodiments, however, assigning a data quality rating to application data may include annotating the application data with the data quality rating. For further explanation, therefore, FIG. 7 sets forth a flow chart illustrating a further exemplary method for qualifying data produced by an application (200) carried out using a plurality of pluggable processing components (210) according to embodiments of the present invention. In the example of FIG. 7, the pluggable processing components (210) combine to carry out a facial recognition application.
The method of FIG. 7 is similar to the method of FIG. 6. That is, the method of FIG. 7 includes: receiving (600), by an application manager, quality metrics (602) for one of the pluggable processing components (210); determining (604), by the application manager, a component quality rating (606) for the pluggable processing component (210) in dependence upon the quality metrics (602); and assigning (608), by the application manager, a data quality rating (612) to application data for the application (200) in dependence upon the component quality rating (606) for the pluggable processing component (210).
In the method of FIG. 7, however, the application manager assigns (608) a data quality rating (612) to application data for the application (200) by annotating (700) the application data with the data quality rating (612). The application manager may annotate (700) the application data with the data quality rating (612) according to the method of FIG. 7 by embedding the data quality rating (612) in the application data produced by the application. For example, consider the application data illustrated in FIG. 7. The application data illustrated in FIG. 7 is implemented as data contained in a data structure demarcated using a pair of <ApplicationOutput> tags. In the example of FIG. 7, the application manager annotates the application data with the data quality rating of ‘Low.’ In such a manner, a process or user receiving the application data illustrated in FIG. 7 is informed that the data quality for that application data is low.
The explanation above with reference to FIG. 7 describes an application manager in which assigning a data quality rating to application data for the application includes annotating the application data with the data quality rating. In some other embodiments, however, assigning a data quality rating to application data may include annotating component data produced by a particular pluggable processing component with the data quality rating. For further explanation, therefore, FIG. 8 sets forth a flow chart illustrating a further exemplary method for qualifying data produced by an application carried out using a plurality of pluggable processing components according to embodiments of the present invention. In the example of FIG. 8, the pluggable processing components (210) combine to carry out a facial recognition application.
The method of FIG. 8 is similar to the method of FIG. 6. That is, the method of FIG. 8 includes: receiving (600), by an application manager, quality metrics (602) for one of the pluggable processing components (210); determining (604), by the application manager, a component quality rating (606) for the pluggable processing component (210) in dependence upon the quality metrics (602); and assigning (608), by the application manager, a data quality rating (612) to application data for the application (200) in dependence upon the component quality rating (606) for the pluggable processing component (210).
In the method of FIG. 8, however, the application manager assigns (608) a data quality rating (612) to application data for the application (200) by annotating (800) component data produced by the pluggable processing component with the data quality rating (612). The application manager may annotate (800) component data produced by the pluggable processing component with the data quality rating (612) according to the method of FIG. 8 by embedding the data quality rating (612) in the application data produced by the application. For example, consider the component data for the face detection component illustrated in FIG. 8. The component data illustrated in FIG. 8 is implemented as data contained in a data structure demarcated using a pair of <FaceDetectOutput> tags. In the example of FIG. 8, the application manager annotates the component data with the data quality rating of ‘Low.’ In such a manner, the other pluggable processing components of the exemplary application (200) or a user reviewing the component data illustrated in FIG. 8 are informed that the data quality for that application data is low.
Exemplary embodiments of the present invention are described largely in the context of a fully functional computer system for qualifying data produced by an application carried out using a plurality of pluggable processing components. Readers of skill in the art will recognize, however, that the present invention also may be embodied in a computer program product disposed on computer readable media for use with any suitable data processing system. Such computer readable media may be transmission media or recordable media for machine-readable information, including magnetic media, optical media, or other suitable media. Examples of recordable media include magnetic disks in hard drives or diskettes, compact disks for optical drives, magnetic tape, and others as will occur to those of skill in the art. Examples of transmission media include telephone networks for voice communications and digital data communications networks such as, for example, Ethernets™ and networks that communicate with the Internet Protocol and the World Wide Web as well as wireless transmission media such as, for example, networks implemented according to the IEEE 802.11 family of specifications. Persons skilled in the art will immediately recognize that any computer system having suitable programming means will be capable of executing the steps of the method of the invention as embodied in a program product. Persons skilled in the art will recognize immediately that, although some of the exemplary embodiments described in this specification are oriented to software installed and executing on computer hardware, nevertheless, alternative embodiments implemented as firmware or as hardware are well within the scope of the present invention.
It will be understood from the foregoing description that modifications and changes may be made in various embodiments of the present invention without departing from its true spirit. The descriptions in this specification are for purposes of illustration only and are not to be construed in a limiting sense. The scope of the present invention is limited only by the language of the following claims.
Patent Application
20090299667
