A performance investigation tool (PIT) is described herein for investigating the performance of a distributed processing system (DPS). The PIT operates by first receiving input information that describes a graph processing task to be executed using a plurality of computing units. The PIT then determines, based on the input information, at least one time-based performance measure that describes the performance of a DPS that is capable of performing the graphical task. More specifically, the PIT can operate in a manual mode to explore the behavior of a specified DPS, or in an automatic mode to find an optimal DPS from within a search space of candidate DPSs. A configuration system may then be used to construct a selected DPS, using the plurality of computing units. In one case, the graph processing task involves training a deep neural network model having a plurality of layers.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for investigating a performance of a distributed processing system and then building the distributed processing system, comprising: receiving, via a computer-implemented performance investigation tool (PIT), input information that describes at least some characteristics of a graph processing task to be executed in a distributed manner using a plurality of computing units; determining, based on the input information, and using the computer-implemented PIT, at least one time-based performance measure that describes a prospective performance of a distributed processing system that is capable of performing the graph processing task using the plurality of computing units; and constructing the distributed processing system using the plurality of computing units. 2. The method of claim 1, wherein the graph processing task comprises training a deep neural network model having a plurality of layers. 3. The method of claim 1, wherein, in a manual mode of operation: the input information describes a particular candidate distributed processing system under consideration, and said determining comprises determining said at least one time-based performance measure of the particular candidate distributed processing system. 4. The method of claim 1, wherein, in an automated mode of operation: the input information describes at least one constraint that the distributed processing system is expected to satisfy, and said determining comprises automatically determining the distributed processing system from among a plurality of candidate distributed candidate systems. 5. The method of claim 4, wherein said determining operates by using a dynamic programming technique to find an optimal distributed processing system. 6. The method of claim 4, wherein the graph processing task comprises training a deep neural network model having a plurality of layers, and wherein said determining comprises at least investigating a number of partitions to be used in each layer of the deep neural network, and an allocation of worker units to the partitions in each layer, in a successive layer-by-layer manner. 7. The method of claim 4, wherein a complexity of said determining is polynomial. 8. The method of claim 1, wherein said receiving of the input information includes executing a test on the computing units to identify at least one time-based performance property of the computing units. 9. The method of claim 1, wherein the graph processing task comprises training a deep neural network model having a plurality of layers, and having a plurality of neurons in each layer, using the plurality of computing units, and wherein said determining comprises: estimating an amount of time to be consumed in performing computations entailed by the training; and estimating an amount of time to be consumed in communicating information within the distributed processing system, in performing the training. 10. The method of claim 9, wherein said estimating of the amount of time to be consumed in performing computations comprises: estimating an amount of time to be consumed in generating activations and error terms, for each layer of the deep neural network model; and estimating an amount of time to be consumed in updating weights, for each layer of the deep neural network model. 11. The method of claim 9, wherein said estimating of the amount of time to be consumed in communicating information comprises: estimating an amount of time, for each layer of the deep neural network model, to be consumed in communicating activations and error terms between computing units; and estimating an amount of time to be consumed in exchanging weight information with at least one parameter module. 12. The method of claim 1, further comprising: based on said at least one time-based performance measure, identifying at least one modification to the distributed processing system to improve performance of the distributed processing system; and making the modification to produce a modified distributed processing system. 13. The method of claim 12, wherein the graph processing task comprises training a deep neural network model having a plurality of layers, and wherein the modified distributed processing system includes: at least one replica unit, each for operating on a replica-specific data set, each said at least one replica unit having at least one worker unit, each worker unit implementing a portion of the deep neural network model; and at least one parameter module, each for exchanging weight information with said at least one replica unit, and the modified distributed processing system further comprising at least one helper worker unit, said at least one helper worker unit being configured to assist at least one helpee worker unit in performing tasks associated with an output layer of the deep neural network model. 14. The method of claim 12, wherein the graph processing task comprises training a deep neural network model having a plurality of layers, and wherein the modified distributed processing system includes: at least one replica unit, each for operating on a replica-specific data set; and at least one parameter module, each for exchanging weight information with said at least one replica unit, wherein each of said at least one replica unit comprises: at least one parameter-interaction worker unit, each of which implements a portion of the deep neural network model, and each of which is configured to exchange weight information with said at least one parameter module; and at least one non-interaction worker unit, each of which implements a portion of the deep neural network model, and each of which is not configured to exchange weight information with said at least one parameter module. 15. The method of claim 14, wherein each replica unit has a single parameter-interaction worker unit. 16. One or more computing devices for implementing at least a portion of a performance investigation framework, comprising: a performance investigation tool (PIT), comprising: an input providing module configured to receive input information that describes at least some aspects of a graph processing task to be executed in a distributed manner using a plurality of computing units; a performance investigation module configured to determine, based on the input information, at least one time-based performance measure that describes a prospective performance of a distributed processing system that is capable of performing the graph processing task using the plurality of computing units; and an output generating module configured to formulate an output which conveys said at least one time-based performance measure. 17. The one or more computing devices of claim 16, wherein, in a manual mode of operation: the input information describes a particular candidate distributed processing system under consideration, and the performance investigation module is configured to determine said at least one time-based performance measure of the particular candidate distributed processing system. 18. The one or more computing devices of claim 16, wherein, in an automated mode of operation: the input information describes at least one constraint that the distributed processing system is expected to satisfy, and the performance investigation module is configured to automatically determine the distributed processing system from among a plurality of candidate distributed candidate systems. 19. The one or more computing devices of claim 16, further comprising a configuration system that is configured to construct the distributed processing system, using the plurality of computing units, based on system configuration information provided by the PIT. 20. A computer readable storage medium for storing computer readable instructions, the computer readable instructions implementing a performance investigation tool (PIT) when executed by one or more processing devices, the computer readable instructions comprising: logic configured to receive input information that describes at least some aspects of a graphical task of training of a deep neural network model having a plurality of layers, to be executed using a plurality of computing units; and logic configured to determine, based on the input information, at least one time-based performance measure that describes performance of a distributed processing system that is capable of performing the graphical task using the plurality of computing units, said at least one time-based performance measure being based on, for each layer of the deep neural network model, at least: an estimated amount of time to be consumed in performing computations entailed by the training; and an estimated amount of time to be consumed in communicating information within the distributed processing system, in performing the training.