Sambanova Systems, Inc.

Junjue Wang

Arvind Krishna Sujeeth

Sumti Jairath

Raghu Prabhakar

Ram Sivaramakrishnan

Matheen Musaddiq

Kaizhao Liang

Tejas Nagendra Babu Nama

Adi Fuchs

Ruddhi Arun Chaphekar

Zhuo Chen

Chen Liu

Bowen Yang

Blaine Rister

Ruddhi Chaphekar

Qingjian Li

David Alan Koeplinger

Yaqi Zhang

Fei Wang

Venkat Krishna Srinivasan

Haocheng Dong

Disclosed is a method that includes sectioning a graph into a sequence of sections, the sequence of sections including at least a first section followed by a second section. The first section is configured to generate a first output in a first target tiling configuration in response to processing a first input in a first input tiling configuration. The graph is configured to reconfigure the first output in the first target tiling configuration to a second input in a second input tiling configuration. The second section is configured to generate a second output in a second target tiling configuration in response to processing the second input in the second input tiling configuration.

Lossless tiling in convolution networks—tiling configuration between two sections

Disclosed is a method that includes generating by an output processing node of a first section of a processing graph, a plurality of output tiles of an output tensor. The plurality of output tiles of the output tensor is written in a memory, where the writing includes zero-padding the plurality of output tiles of the output tensor in the memory. The zero-padded plurality of output tiles of the output tensor are tiled, to generate a plurality of input tiles of an input tensor. The plurality of input tiles of the input tensor is processed in a second section of the processing graph.

Lossless tiling in convolution networks—padding and re-tilling at section boundaries

In a method, in response to an interface a computer-implemented analysis assistant initiates a presentation of inefficiency results, determined an efficiency analyzer based on a mapping of a dataflow program to execute on hardware of a computing system. The assistant receives an inefficiency included among the inefficiency results and composes formatted inefficiency results comprising a presentation format of the inefficiency to assist a developer of the dataflow program to interpret the inefficiency. The analysis assistant outputs the formatted inefficiency results to an interface, which can comprise an interface to output the formatted inefficiency results for use by the developer to improve the dataflow program in association with the inefficiency. In implementations the presentation can comprise an interactive presentation with a developer of the dataflow program. A computer program product and a computing system can implement the method. The computing system can execute the assistant, and can include the interfaces.

Analysis assistant for determining execution inefficiencies in dataflow programs

In a method a computer-implemented efficiency analyzer selects operators from an intermediate representation of a dataflow program. The operators are included in a mapping of the operators to hardware of a computing system to execute the dataflow program. Based on the mapping and a description of the hardware, the efficiency analyzer computes an execution metric associated with executing the operators on the hardware. Based on the execution metric and hardware description, the efficiency analyzer determines an inefficiency metric, and based on the inefficiency metric, the efficiency analyzer determines an inefficiency associated with the dataflow program. The computing system to execute the dataflow program can comprise a coarse grain computing system and the hardware can include a reconfigurable processor of the computing system. A computer program product and a computing system to a the dataflow program can implement the method.

Method and system to determine execution inefficiencies in dataflow programs

According to a computing method a compiler determines a recompute node included in a dataflow application and a checkpoint tensor produced by the recompute node. The compiler determines a recompute cost to recompute the checkpoint tensor, and a memory cost to checkpoint the checkpoint tensor in a memory. Based on the recompute cost and/or the memory cost, the compiler determines a solution cost and compares the solution cost to a solution threshold. Based on comparing the solution cost to the solution threshold, the compiler determines a checkpoint solution to execute the dataflow application. The checkpoint solution can comprise recomputing or checkpointing the checkpoint tensor. In some implementations, the compiler can determine a recompute ratio of the recompute cost to the memory cost and can compare the recompute ratio to the solution threshold. A computer program product and a computing system can implement aspects of the method.

Tensor checkpoint optimization in dataflow computing applications

In a method a compiler performs a trial compilation to a low level (LL) intermediate representation (IR) of a high level (HL) decision to execute a dataflow application on a computing system. The LLIR comprises hardware resources to execute the application based on the HL decision and the compiler determines a trial result based on LL execution metrics associated with the trail compilation. The compiler performs a trial compilation of a second HL decision to a second LLIR and determines a trial result based on LL execution metrics associated with the second trail compilation. The compiler evaluates the trial results and, based on the evaluations, selects one or both of the HL decisions for executing the dataflow application. A computer program product and a computing system can implement the method.

Iterative compilation to optimize translation in reconfigurable dataflow architectures

A data processing system includes memory and reconfigurable processors, operatively coupled to the memory, configured to execute a sequence of subgraphs of a graph. The sequence of subgraphs includes a preceding subgraph and a succeeding subgraph. The data processing system also includes data flow logic, operatively coupled to the reconfigurable processors and the memory, configured to store a tiled output of the preceding subgraph as a composed input in the memory and make available parts of the composed input for processing by the succeeding subgraph.

Lossless tiling in convolution networks—data flow logic

A data processing system receives a graph that includes a sequence of layers and executes graph cuts between a preceding layer in the graph and a succeeding layer in the graph that succeeds the preceding layer. The preceding layer generates a set of tiles on a tile-by-tile basis and the succeeding layer processes a tensor that includes multiple tiles in the set of tiles. Thus the graph is partitioned into a sequence of subgraphs, and a subgraph in the sequence of subgraphs including a sub-sequence of layers in the sequence of layers. One or more configuration files is generated to configure runtime logic to execute the sequence of subgraphs and the one or more configuration files are stored on a computer-readable media.

Lossless tiling in convolution networks—section cuts

A data processing system includes compile time logic to section a graph into a sequence of sections, including a first section followed by a second section. The compile time logic configured the first section to generate a first output in a first non-overlapping target configuration in response to processing an input in a first overlapping input configuration, and configures the second section to generate a second output in a second non-overlapping target configuration in response to processing the first output in a second overlapping input configuration. The compile time logic also creates a set of computer instructions to execute the first section and the second section on a target processing system.

Lossless tiling in convolution networks—resetting overlap factor to zero at section boundaries

Disclosed is a data processing system that includes a plurality of reconfigurable processors and processor memory. Runtime logic, operatively coupled to the plurality of reconfigurable processors and the processor memory, is configured to configure at least one reconfigurable processor in the plurality of reconfigurable processors with a first subgraph in a sequence of subgraphs of a graph; load an input onto the processor memory; on a tile-by-tile basis, process a first set of input tiles from the input through the first subgraph and generate a first set of intermediate tiles, load the first set of intermediate tiles onto the processor memory, and process the first set of intermediate tiles through the first subgraph and generate a first set of output tiles; and compose output tiles in the first set of output tiles into a first composed input, and load the first composed input onto the processor memory.

Lossless tiling in convolution networks—materialization of tensors

A processing graph of an application with a sequence of processing nodes is obtained which processes an input and generates an intermediate representation a further intermediate representation, and an output representation of the input at stages in the sequence of processing nodes. Graph metadata is generated that specifies a non-overlapping target tiling configuration for the output representation, an overlapping tiling configuration for the input, an overlapping tiling configuration for the intermediate representation, and a third tiling configuration for the further intermediate representation. The processing graph is modified based on the graph metadata to conform to the parameters specified by the graph metadata. A set of computer instructions is then created to execute the modified processing graph on a target processing system.

Lossless tiling in convolution networks—graph metadata generation

Disclosed is a data processing system that includes compile time logic to section a graph into a sequence of sections including a first section and a second section. The compile time logic is to configure the first section with a first topology of tiling configurations in which to tile inputs, intermediate outputs, and final outputs of the first section, and configure the second section with a second topology of tiling configurations in which to tile inputs, intermediate outputs, and final outputs of the second section. The data processing system further includes runtime logic configured with the compile time logic to execute the first section to generate the inputs, intermediate outputs, and final outputs of the first section in the first topology of tiling configurations, and execute the second section to generate the inputs, intermediate outputs, and final outputs of the second section in the second topology of tiling configurations.

Lossless tiling in convolution networks—tiling configuration for a sequence of sections of a graph

Disclosed is a data processing system to receive a processing graph of an application. A compile time logic is configured to modify the processing graph and generate a modified processing graph. The modified processing graph is configured to apply a post-padding tiling after applying a cumulative input padding that confines padding to an input. The cumulative input padding pads the input into a padded input. The post-padding tiling tiles the padded input into a set of pre-padded input tiles with a same tile size, tiles intermediate representation of the input into a set of intermediate tiles with a same tile size, and tiles output representation of the input into a set of non-overlapping output tiles with a same tile size. Runtime logic is configured with the compile time logic to execute the modified processing graph to execute the application.

Lossless tiling in convolution networks-backward pass

Lossless tiling in convolution networks&#x2014;padding before tiling, location-based tiling, and zeroing-out

Disclosed is a data processing system which includes compile time logic configured to section a graph into a sequence of subgraphs, the sequence of subgraphs including at least a first subgraph. The compile time logic configures the first subgraph to generate a plurality of output tiles of an output tensor. A runtime logic configured with the compile time logic is to execute the sequence of subgraphs to generate, at the output of the first subgraph, the plurality of output tiles of the output tensor, and write the plurality of output tiles in a memory in an overlapping configuration. In an example, an overlapping region between any two neighboring output tiles of the plurality of output tiles comprises a summation of a corresponding region of a first neighboring output tile and a corresponding region of a second neighboring output tile.

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San Mateo, CA, United States of America

Junjue Wang