Single-step collective operations

Information

  • Patent Grant
  • 11922237
  • Patent Number
    11,922,237
  • Date Filed
    Sunday, February 5, 2023
    a year ago
  • Date Issued
    Tuesday, March 5, 2024
    2 months ago
Abstract
A method for collective communications includes invoking a collective operation over a group of computing processes in which the processes concurrently transmit and receive data to and from other processes in the group via a communication medium. Messages are composed for transmission by source processes including metadata indicating how the data to be transmitted by the source processes in the collective operation are to be handled by destination processes that are to receive the data and also including in at least some of the messages the data to be transmitted by one or more of the source processes to one or more of the destination processes. The composed messages are transmitted concurrently from the source processes to the destination processes in the group over the communication medium. The data are processed by the destination processes in response to the metadata included in the messages received by the destination processes.
Description
FIELD

The present invention relates generally to high-performance computing (HPC), and particularly to communication among collaborating software processes using collective operations.


BACKGROUND

Collective communications are used by groups of computing nodes to exchange data in connection with a distributed processing application. In HPC, for example, the nodes are typically software processes running in parallel, for example on different computing cores. The nodes exchange collective communications with one another in connection with parallel program tasks carried out by the processes. The term “collective operation” is used in the present description and in the claims to refer to functions performed concurrently by multiple processes (and possibly all the processes) participating in a parallel processing task. These collective operations typically include communication functions, in which the processes in the group concurrently transmit and receive data to and from other processes in the group via a communication medium. These functions are referred to as “collective communications.” The collective communications among processes may be exchanged over any suitable communication medium, such as over a physical network, for example a high-speed switch fabric or packet network, or via shared memory within a computer.


Various protocols have been developed to support collective communications. One of the best-known protocols is the Message Passing Interface (MPI), which enables processes to move data from their own address spaces to the address spaces of other processes through cooperative operations carried out by each process in a process group. In MPI parlance, the process group is referred to as a “communicator,” and each member process is identified as a “rank.” MPI collective operations include all-to-all, all-to-all-v, and all-to-all-w operations, which gather and scatter data from all ranks to all other ranks in a communicator. In the operation all-to-all, each process in the communicator sends a fixed-size message to each of the other processes. The operations all-to-all-v and all-to-all-w are similar to the operation all-to-all, but the messages may differ in size. In all-to-all-w, the messages may also contain different data types.


In naïve implementations of all-to-all-v and all-to-all-w, each member process transmits messages to all other member processes in the group. In large-scale HPC distributed applications, the group can include thousands of processes running on respective processing cores, meaning that millions of messages are exchanged following each processing stage. To reduce the communication burden associated with this message exchange, message aggregation protocols have been proposed.


For example, U.S. Pat. No. 10,521,283 describes in-node aggregation of MPI all-to-all and all-to-all-v collectives. An MPI collective operation is carried out in a fabric of network elements by transmitting MPI messages from all the initiator processes in an initiator node to designated responder processes in respective responder nodes. Respective payloads of the MPI messages are combined in a network interface device of the initiator node to form an aggregated MPI message. The aggregated MPI message is transmitted through the fabric to network interface devices of responder nodes, which disaggregate the aggregated MPI message into individual messages and distribute the individual messages to the designated responder node processes.


SUMMARY

Embodiments of the present invention that are described hereinbelow provide efficient methods for collective communications, as well as systems and software implementing such methods.


There is therefore provided, in accordance with an embodiment of the invention, a method for collective communications, which includes invoking a collective operation over a group of computing processes in which the processes in the group concurrently transmit and receive data to and from other processes in the group via a communication medium. Messages are composed for transmission by source processes in the group, including metadata indicating how the data to be transmitted by the source processes in the collective operation are to be handled by destination processes that are to receive the data and also including in at least some of the messages the data to be transmitted by one or more of the source processes to one or more of the destination processes. The composed messages are transmitted concurrently from the source processes to the destination processes in the group over the communication medium. The data are processed by the destination processes in response to the metadata included in the messages received by the destination processes.


In a disclosed embodiment, composing the messages includes incorporating the data together with the metadata in the messages when a size of the data for transmission from a given source process to a given destination process is less than a predefined threshold, and transmitting only the metadata without the data when the size of the data for transmission from the given source process to the given destination process is greater than the predefined threshold, whereby the destination process retrieves the data from the source process using the metadata in the messages.


Additionally or alternatively, transmitting the composed messages includes, for at least some of the messages, transmitting the messages having respective sizes that are greater than a predefined threshold to the destination processes without aggregation, and aggregating the messages for which the respective sizes are less than the predefined threshold and transmitting the aggregated messages to the destination processes.


In some embodiments, processing the data includes transmitting, responsively to the metadata in a message received by a destination process, a request from the destination process to the source process to convey the data from the source process to the destination process, whereby the source process transmits the data to the destination process responsively to the request. In a disclosed embodiment, transmitting the request includes transmitting a remote direct memory access (RDMA) read request from the destination process to the source process.


In some embodiments, invoking the collective operation includes initiating an all-to-all-v, all-to-all-w, all-gather-v, gather-v, or scatter-v operation.


There is also provided, in accordance with an embodiment of the invention, a system for collective communications, including multiple processors, which are interconnected by a communication medium and are programmed to run respective computing processes. Upon receiving an invocation of a collective operation over a group of the processes in which the processes in the group are to concurrently transmit and receive data to and from other processes in the group via a communication medium, the processors compose, for transmission by source processes in the group, messages including metadata indicating how the data to be transmitted by the source processes in the collective operation are to be handled by destination processes that are to receive the data and also including in at least some of the messages the data to be transmitted by one or more of the source processes to one or more of the destination processes. The processors transmit the composed messages concurrently from the source processes to the destination processes in the group over the communication medium. The processors process the data in the destination processes in response to the metadata included in the messages received by the destination processes.


There is additionally provided, in accordance with an embodiment of the invention, a computer software product for collective communications among a group of computing processes running on processors, which are interconnected by a communication medium. The product includes a tangible, non-transitory computer-readable medium in which program instructions are stored, which instructions cause the processors, upon receiving an invocation of a collective operation over a group of the processes in which the processes in the group are to concurrently transmit and receive data to and from other processes in the group via a communication medium, to compose, for transmission by source processes in the group, messages including metadata indicating how the data to be transmitted by the source processes in the collective operation are to be handled by destination processes that are to receive the data and also including in at least some of the messages the data to be transmitted by one or more of the source processes to one or more of the destination processes. The instructions cause the processors to transmit the composed messages concurrently from the source processes to the destination processes in the group over the communication medium, and to process the data in the destination processes in response to the metadata included in the messages received by the destination processes.


The present invention will be more fully understood from the following detailed description of the embodiments thereof, taken together with the drawings in which:





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 is a block diagram that schematically illustrates collective communications in a computer system, in accordance with an embodiment of the invention;



FIG. 2 is a flow chart that schematically illustrates a method for collective communications, in accordance with an embodiment of the invention; and



FIGS. 3 and 4 are ladder diagrams that schematically illustrate collective communication exchanges between source and destination processes, in accordance with embodiments of the invention.





DETAILED DESCRIPTION
Overview

In collective communications, destination processes do not necessarily have information about the messages that they are to receive from source processes until the messages actually arrive. In all-to-all-v operations, for example, the processes in a group may not have any advance indication of their own as to which source processes are going to send them data in a given operation or how much data a given source process is going to send. This uncertainty can lead to bottlenecks and inefficiencies in handling the data at the destination processes.


In some applications, this uncertainty is alleviated by performing two collective operations in each round of communications: In the first operation, the processes exchange metadata to inform the destination processes how much data to expect, of what type, and from which source processes. This first operation can be carried out using all-to-all operation, for example. In the second operation, such as an all-to-all-v operation, the processes exchange the actual data. This two-stage model makes the programmer's job easier, but at the cost of increased communication load and latency.


Embodiments of the present invention that are described herein address the shortcomings of these existing models by enabling the processes in a group to exchange both metadata and data in a single collective operation. When a collective operation is invoked over the group of computing processes, the source processes in the group identify the data that they are going to transmit to respective destination processes, along with metadata indicating how the data are to be handled by each destination process. The source processes compose messages containing the metadata for transmission to the respective destination processes. Some of the messages may comprise only metadata, for example messages to destination nodes for which the source node has a large volume of data to transmit. Such messages enable the destination process to prepare an application buffer to receive the data before the actual transmission of the data. Other messages, however, comprise the actual data to be transmitted to a given destination processes, together with the metadata.


The source processes concurrently transmit these messages (including messages containing only metadata and messages containing both metadata and data) to the destination processes in the group over a communication medium. Upon receiving the messages, the destination processes use the metadata comprised in the messages that they have received in processing the data.


Some embodiments are directed particularly to implementing collective operations in which message sizes are not known in advance, such as all-to-all-v, all-to-all-w, all-gather-v, gather-v, and scatter-v. In such operations, the source process will incorporate the data together with the metadata in a given message when the size of the data for transmission from the source process to a given destination process is less than a predefined threshold. On the other hand, when the size of the data for transmission from the source process to a given destination process is greater than the predefined threshold, the source process will transmit only the metadata without the data in the message that it sends in the collective operation. The destination process will then use the metadata that it received in the message in retrieving the data from the source process in an additional step following the collective message exchange. For example, the destination process may use the metadata in transmitting a request to the source process, such as a remote direct memory access (RDMA) read request, to transmit the data to the destination process.


In some embodiments, message aggregation is used to enhance communication efficiency in handling small messages. In these embodiments, messages having respective sizes that are greater than a predefined threshold are transmitted from the source processes to the destination processes directly, without aggregation. Messages smaller than the predefined threshold, however, are aggregated by the source processes before transmission of the aggregated messages to the destination processes. Various aggregation algorithms may be used for this purpose. In one embodiment, a multi-step aggregation algorithm is used, in which data blocks are scattered and gathered among different processes at each step. Most data blocks that are aggregated are transmitted multiple times, through some are sent only once,


Although the present embodiments are described specifically with reference to the all-to-all-v operations, the principles of these embodiments may similarly be applied in accelerating other collective operations in which message sizes are not known in advance, as noted above. Furthermore, although these embodiments are framed in terms of MPI operations and protocols, the principles of the present invention may alternatively be implemented, mutatis mutandis, in conjunction with other protocols. All such alternative implementations are considered to be within the scope of the present invention.


System Description


FIG. 1 is a block diagram that schematically illustrates collective communications in a computer system 20, in accordance with an embodiment of the invention. System 20 in this example comprises multiple processors, in the form of host computers 22, each comprising a central processing unit (CPU), memory, and other components as are known in the art. Each host computer 22 is connected by a respective network interface controller (NIC) 24 to a network 26, such as a high-speed switch fabric or other packet communication network. Although for the sake of simplicity only three host computers are shown in FIG. 1, in practice system 20 may comprise hundreds or thousands of host computers, interconnected by network 26. Host computers 22 in system 20 run a distributed HPC software application, in which multiple processes 28 run in parallel on different host computers. Typically (although not necessarily), each host computer 22 comprises multiple CPU cores, and each process 28 is assigned to run on a respective core.


Following certain computational stages in the distributed application, the program instructions invoke a collective operation, such as an all-to-all-v operation. In the context of this collective operation, system 20 defines an MPI communicator including all the participating processes 28, and each process has a respective rank within the communicator. In response to these instructions, each process 28 prepares data for transmission to all the other processes (ranks) within system 20, as well as metadata indicating how the data to be transmitted by the source processes in the collective operation are to be handled by destination processes that are to receive the data.


When the size of the data for transmission to a given destination is less than a certain threshold data size, the source process incorporates the data in a message 30 containing both the data and the metadata for transmission to the destination process. Messages 30 that are smaller than a predefined message aggregation threshold are aggregated and transmitted together to the respective destinations, typically via one or more intermediate destinations. This message aggregation can be carried out, for example, using techniques described in U.S. patent application Ser. No. 18/074,563, filed Dec. 5, 2022, whose disclosure is incorporated herein by reference. Alternatively or additionally, the message aggregation may take advantage of capabilities of NICs 24 in supporting collective operations, for example as described in the above-mentioned U.S. Pat. No. 10,521,283.


On the other hand, when the size of the data for transmission to a given destination process is greater than the threshold data size, the source process transmits a message 32 containing only the metadata to the respective destination process. The destination process uses the metadata in preparing a buffer in local memory to receive the data and in sending a request to the source process to transmit the data. Alternatively, message 32 may also contain a part of the data, in addition to the metadata.


Host computers 22 carry out the collective operations that are described herein, including particularly the present methods of selective data transmission and aggregation, under the control of software instructions. The software for these purposes may be downloaded to the host computers in electronic form, for example over network 26. Additionally or alternatively, the software may be stored on tangible, non-transitory computer-readable media, such as optical, magnetic, or electronic memory media.


Methods for Transmitting Data and Metadata


FIG. 2 is a flow chart that schematically illustrates a method for collective communications, in accordance with an embodiment of the invention. This method is carried out by a group of processes, such as processes 28 in system 20 (FIG. 1), when a collective operation is invoked over the group, at a collective initiation step 40.


In response to invocation of the collective operation, each of at least some of the processes in the group generates data for transmission to some or all of the other processes, at a data generation step 42. Typically, in any given collective operation, each process 28 can be both a source process for transmission of data and a destination process for receiving data. (When messages are aggregated, the destination processes also serve as intermediate destinations, which receive data in transit for aggregation and forwarding.) At step 42, the source processes also generate metadata that indicate how the data to be transmitted by the source processes in the collective operation are to be handled by the respective destination processes upon receiving the data. The metadata typically indicate the data size and may also provide information about the data type and/or the location of the data in a source buffer. In FIG. 2, the source processes are indicated by the index J, while the destination processes are indicated by the index K, so that the message to be sent from a given source process to a given destination process is identified as MESSAGE(J,K).


Each source process J evaluates the size of each of the messages (J,K) that the source process has prepared, at an aggregation evaluation step 44. The size in this case refers to the combined size of the respective metadata and data that are to be transmitted to each destination process K. If this size is less than a predefined aggregation threshold, for example less than 500 bytes, the source process will not transmit the message directly to the destination process but will rather aggregate the message with messages from other source processes that are directed to the same destination process, at an aggregation step 46. Any suitable message aggregation algorithm may be used for this purpose, such as the algorithms described in the above-mentioned U.S. patent application Ser. No. 18/074,563. Depending on the type and radix of the aggregation algorithm, the aggregating processes determine when aggregation of a given set of messages is complete, at an aggregation completion step 47. The aggregated messages, including data and metadata, are then transmitted to the destination processes, at an aggregated transmission step 48.


When the message (J,K) is larger than the aggregation threshold, the source process assesses whether the size of the data to be transmitted to destination process K is larger than a direct data transmission threshold, at a data evaluation step 50. If the data size is less than this threshold, the source process frames the data and metadata together in a single message and transmits the message to the destination process as part of the collective operation, at a combined transmission step 52.


When the size of the data destined for a given process K is found at step 50 to be larger than the direct data transmission threshold, the source process transmits only the metadata to process K as part of the collective operation, at a metadata transmission step 54. The source process then goes on to process the data for transmission to the next destination process K+1. After receiving the message sent at step 54, process K extracts the metadata from the message and uses the metadata in composing and transmitting a data request to the source process J, at a data request step 56. Upon receiving this request, the source process transmits the appropriate data to destination process K, at a data transmission step 58.



FIG. 3 is a ladder diagram that schematically shows details of an implementation of combined transmission step 52 (FIG. 2) in transferring data and metadata between a source 60 and a destination 62, in accordance with an embodiment of the invention. This diagram illustrates how messages containing both data and metadata are handled at destination 62. This implementation uses a library 64 of input/output and memory handling routines in receiving incoming messages and in transferring the data from the messages to a user space 66 where the destination process can access the data.


Source 60 transmits a combined message 68 containing data and metadata to destination 62. A routine in library 64 extracts the metadata from message 68, allocates a memory buffer 70 based on the metadata, and saves the incoming data in the buffer. Library 64 transfers data 72 from buffer 70 to a process memory 74 in user space 66, for input to the destination process, along with the data received from other source processes.



FIG. 4 is a ladder diagram that schematically shows details of an implementation of steps 54, 56, and 58 (FIG. 2), in conveying data from source 60 to destination 62, in accordance with another embodiment of the invention. This diagram illustrates how a data transfer is carried out when the size of the data is too large to be transmitted together with the metadata in the collective operation.


At step 54, source 60 transmits a message to destination 62 containing the metadata with respect to the data to be transferred. Destination 62 calls a library routine 80 to handle the data transfer based on the metadata. The library routine allocates a buffer in process memory 74 and transmits a data request to source 60 at step 56. For example, the data request may comprise an RDMA read request sent by NIC 24 (FIG. 1) of the destination computer and directed to an address in the memory of source 60 that was indicated by the metadata received by library 64 at step 54. NIC 24 of source 60 reads the data from the source memory and transmits an RDMA read response containing the data to destination 62 at step 58. Upon receiving the RDMA read response, the NIC of destination 62 will write the data directly to the designated buffer in process memory 74.


Alternatively, destination 62 may use other protocols to request transfer of the data from source 60. For example, destination 62 may send a message to source 60 at step 56 indicating the address of the designated buffer in process memory 74, and requesting data transfer to this buffer. NIC 24 of source 60 can then direct an RDMA write request containing the data to the designated buffer. Other implementations will be apparent to those skilled in the art after reading the above description and are considered to be within the scope of the present invention.


It will thus be appreciated that the embodiments described above are cited by way of example, and the present invention is not limited to what has been particularly shown and described hereinabove. Rather, the scope of the present invention includes both combinations and subcombinations of the various features described hereinabove, as well as variations and modifications thereof which would occur to persons skilled in the art upon reading the foregoing description and which are not disclosed in the prior art.

Claims
  • 1. A method for collective communications, comprising: invoking a collective operation over a group of computing processes in which the processes in the group concurrently transmit and receive data to and from other processes in the group via a communication medium;composing, for transmission by source processes in the group, messages comprising metadata indicating how the data to be transmitted by the source processes in the collective operation are to be handled by destination processes that are to receive the data and also comprising in at least some of the messages the data to be transmitted by one or more of the source processes to one or more of the destination processes,such that when a size of the data for transmission from a given source process to a given destination process is less than a predefined threshold, the data is incorporated together with the metadata in a message from the given source process to the given destination process, and when the size of the data for transmission from the given source process to the given destination process is greater than the predefined threshold, the message contains only the metadata without the data;transmitting the composed messages concurrently from the source processes to the destination processes in the group over the communication medium; andprocessing the data by the destination processes in response to the metadata comprised in the messages received by the destination processes, wherein when a given message contains the metadata without the data, the destination process retrieves the data from the source process using the metadata in the given message.
  • 2. The method according to claim 1, wherein transmitting the composed messages comprises, for at least some of the messages: transmitting the messages having respective sizes that are greater than a predefined threshold to the destination processes without aggregation; andaggregating the messages for which the respective sizes are less than the predefined threshold and transmitting the aggregated messages to the destination processes.
  • 3. The method according to claim 1, wherein processing the data comprises transmitting, responsively to the metadata in a message received by a destination process, a request from the destination process to the source process to convey the data from the source process to the destination process, whereby the source process transmits the data to the destination process responsively to the request.
  • 4. The method according to claim 3, wherein transmitting the request comprises transmitting a remote direct memory access (RDMA) read request from the destination process to the source process.
  • 5. The method according to claim 1, wherein invoking the collective operation comprises initiating an all-to-all-v, all-to-all-w, all-gather-v, gather-v, or scatter-v operation.
  • 6. A system for collective communications, comprising multiple processors, which are interconnected by a communication medium and are programmed to run respective computing processes such that upon receiving an invocation of a collective operation over a group of the processes in which the processes in the group are to concurrently transmit and receive data to and from other processes in the group via a communication medium, the processors compose, for transmission by source processes in the group, messages comprising metadata indicating how the data to be transmitted by the source processes in the collective operation are to be handled by destination processes that are to receive the data and also comprising in at least some of the messages the data to be transmitted by one or more of the source processes to one or more of the destination processes,such that when a size of the data for transmission from a given source process to a given destination process is less than a predefined threshold, the data is incorporated together with the metadata in a message from the given source process to the given destination process, and when the size of the data for transmission from the given source process to the given destination process is greater than the predefined threshold, the message contains only the metadata without the data, andthe processors transmit the composed messages concurrently from the source processes to the destination processes in the group over the communication medium, and process the data in the destination processes in response to the metadata comprised in the messages received by the destination processes, wherein when a given message contains the metadata without the data, the destination process retrieves the data from the source process using the metadata in the given message.
  • 7. The system according to claim 6, wherein for at least some of the messages, the processors are to transmit the messages having respective sizes that are greater than a predefined threshold to the destination processes without aggregation, and the processors are to aggregate the messages for which the respective sizes are less than the predefined threshold and transmit the aggregated messages to the destination processes.
  • 8. The system according to claim 6, wherein the processors are to transmit, responsively to the metadata in a message received by a destination process, a request from the destination process to the source process to convey the data from the source process to the destination process, whereby the source process transmits the data to the destination process responsively to the request.
  • 9. The system according to claim 8, wherein the request comprises a remote direct memory access (RDMA) read request transmitted from the destination process to the source process.
  • 10. The system according to claim 6, wherein the collective operation comprises an all-to-all-v, all-to-all-w, all-gather-v, gather-v, or scatter-v operation.
  • 11. A computer software product for collective communications among a group of computing processes running on processors, which are interconnected by a communication medium, the product comprising a tangible, non-transitory computer-readable medium in which program instructions are stored, which instructions cause the processors, upon receiving an invocation of a collective operation over a group of the processes in which the processes in the group are to concurrently transmit and receive data to and from other processes in the group via a communication medium: to compose, for transmission by source processes in the group, messages comprising metadata indicating how the data to be transmitted by the source processes in the collective operation are to be handled by destination processes that are to receive the data and also comprising in at least some of the messages the data to be transmitted by one or more of the source processes to one or more of the destination processes,such that when a size of the data for transmission from a given source process to a given destination process is less than a predefined threshold, the data is incorporated together with the metadata in a message from the given source process to the given destination process, and when the size of the data for transmission from the given source process to the given destination process is greater than the predefined threshold, the message contains only the metadata without the data, andto transmit the composed messages concurrently from the source processes to the destination processes in the group over the communication medium, and to process the data in the destination processes in response to the metadata comprised in the messages received by the destination processes, wherein when a given message contains the metadata without the data, the destination process retrieves the data from the source process using the metadata in the given message.
  • 12. The product according to claim 11, wherein for at least some of the messages, the instructions cause the processors to transmit the messages having respective sizes that are greater than a predefined threshold to the destination processes without aggregation, and to aggregate the messages for which the respective sizes are less than the predefined threshold and transmit the aggregated messages to the destination processes.
  • 13. The product according to claim 11, wherein the instructions cause the processors to transmit, responsively to the metadata in a message received by a destination process, a request from the destination process to the source process to convey the data from the source process to the destination process, whereby the source process transmits the data to the destination process responsively to the request.
  • 14. The product according to claim 13, wherein the request comprises a remote direct memory access (RDMA) read request transmitted from the destination process to the source process.
  • 15. The product according to claim 11, wherein the collective operation comprises an all-to-all-v, all-to-all-w, all-gather-v, gather-v, or scatter-v operation.
CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Patent Application 63/405,505, filed Sep. 12, 2022, which is incorporated herein by reference.

US Referenced Citations (293)
Number Name Date Kind
4933969 Marshall et al. Jun 1990 A
5068877 Near et al. Nov 1991 A
5325500 Bell et al. Jun 1994 A
5353412 Douglas et al. Oct 1994 A
5404565 Gould et al. Apr 1995 A
5408469 Opher et al. Apr 1995 A
5606703 Brady et al. Feb 1997 A
5944779 Blum Aug 1999 A
6041049 Brady Mar 2000 A
6115394 Balachandran et al. Sep 2000 A
6212197 Christensen et al. Apr 2001 B1
6370502 Wu et al. Apr 2002 B1
6438137 Turner et al. Aug 2002 B1
6483804 Muller et al. Nov 2002 B1
6507562 Kadansky et al. Jan 2003 B1
6728862 Wilson Apr 2004 B1
6816492 Turner et al. Nov 2004 B1
6857004 Howard et al. Feb 2005 B1
6937576 Di Benedetto et al. Aug 2005 B1
7102998 Golestani Sep 2006 B1
7124180 Ranous Oct 2006 B1
7164422 Wholey, III et al. Jan 2007 B1
7171484 Krause et al. Jan 2007 B1
7224669 Kagan et al. May 2007 B2
7245627 Goldenberg et al. Jul 2007 B2
7313582 Bhanot et al. Dec 2007 B2
7327693 Rivers et al. Feb 2008 B1
7336646 Muller Feb 2008 B2
7346698 Hannaway Mar 2008 B2
7555549 Campbell et al. Jun 2009 B1
7613774 Caronni et al. Nov 2009 B1
7636424 Halikhedkar et al. Dec 2009 B1
7636699 Stanfill Dec 2009 B2
7676597 Kagan et al. Mar 2010 B2
7738443 Kumar Jun 2010 B2
7760743 Shokri et al. Jul 2010 B2
8213315 Crupnicoff et al. Jul 2012 B2
8255475 Kagan et al. Aug 2012 B2
8370675 Kagan Feb 2013 B2
8380880 Gulley et al. Feb 2013 B2
8510366 Anderson et al. Aug 2013 B1
8645663 Kagan et al. Feb 2014 B2
8738891 Karandikar et al. May 2014 B1
8761189 Shachar et al. Jun 2014 B2
8768898 Trimmer et al. Jul 2014 B1
8775698 Archer et al. Jul 2014 B2
8811417 Bloch et al. Aug 2014 B2
9110860 Shahar Aug 2015 B2
9189447 Faraj Nov 2015 B2
9294551 Froese et al. Mar 2016 B1
9344490 Bloch et al. May 2016 B2
9397960 Arad et al. Jul 2016 B2
9456060 Pope et al. Sep 2016 B2
9563426 Bent et al. Feb 2017 B1
9626329 Howard Apr 2017 B2
9756154 Jiang Sep 2017 B1
10015106 Florissi et al. Jul 2018 B1
10027601 Narkis et al. Jul 2018 B2
10158702 Bloch et al. Dec 2018 B2
10187400 Castro et al. Jan 2019 B1
10284383 Bloch et al. May 2019 B2
10296351 Kohn et al. May 2019 B1
10305980 Gonzalez et al. May 2019 B1
10318306 Kohn et al. Jun 2019 B1
10320508 Shimizu et al. Jun 2019 B2
10425350 Florissi Sep 2019 B1
10515045 Mattina Dec 2019 B1
10521283 Shuler et al. Dec 2019 B2
10528518 Graham et al. Jan 2020 B2
10541938 Timmerman et al. Jan 2020 B1
10547553 Shattah et al. Jan 2020 B2
10621489 Appuswamy et al. Apr 2020 B2
10727966 Izenberg et al. Jul 2020 B1
11088971 Brody et al. Aug 2021 B2
11336383 Mula et al. May 2022 B2
11388263 Levi et al. Jul 2022 B2
20020010844 Noel et al. Jan 2002 A1
20020035625 Tanaka Mar 2002 A1
20020150094 Cheng et al. Oct 2002 A1
20020150106 Kagan et al. Oct 2002 A1
20020152315 Kagan et al. Oct 2002 A1
20020152327 Kagan et al. Oct 2002 A1
20020152328 Kagan et al. Oct 2002 A1
20020165897 Kagan et al. Nov 2002 A1
20030002483 Zwack Jan 2003 A1
20030018828 Craddock et al. Jan 2003 A1
20030061417 Craddock et al. Mar 2003 A1
20030065856 Kagan et al. Apr 2003 A1
20030120835 Kale et al. Jun 2003 A1
20040030745 Boucher et al. Feb 2004 A1
20040062258 Grow et al. Apr 2004 A1
20040078493 Blumrich et al. Apr 2004 A1
20040120331 Rhine et al. Jun 2004 A1
20040123071 Stefan et al. Jun 2004 A1
20040174820 Ricciulli Sep 2004 A1
20040252685 Kagan et al. Dec 2004 A1
20040260683 Chan et al. Dec 2004 A1
20050097300 Gildea et al. May 2005 A1
20050122329 Janus Jun 2005 A1
20050129039 Biran et al. Jun 2005 A1
20050131865 Jones et al. Jun 2005 A1
20050223118 Tucker et al. Oct 2005 A1
20050281287 Ninomi et al. Dec 2005 A1
20060095610 Arndt et al. May 2006 A1
20060282838 Gupta et al. Dec 2006 A1
20070127396 Jain et al. Jun 2007 A1
20070127525 Sarangam et al. Jun 2007 A1
20070162236 Lamblin et al. Jul 2007 A1
20080040792 Larson et al. Feb 2008 A1
20080104218 Liang et al. May 2008 A1
20080126564 Wilkinson May 2008 A1
20080168471 Benner et al. Jul 2008 A1
20080181260 Vonog et al. Jul 2008 A1
20080192750 Ko et al. Aug 2008 A1
20080219159 Chateau et al. Sep 2008 A1
20080244220 Lin et al. Oct 2008 A1
20080263329 Archer et al. Oct 2008 A1
20080288949 Bohra et al. Nov 2008 A1
20080298380 Rittmeyer et al. Dec 2008 A1
20080307082 Cai et al. Dec 2008 A1
20090037377 Archer et al. Feb 2009 A1
20090063816 Arimilli et al. Mar 2009 A1
20090063817 Arimilli et al. Mar 2009 A1
20090063891 Arimilli et al. Mar 2009 A1
20090182814 Tapolcai et al. Jul 2009 A1
20090240838 Berg et al. Sep 2009 A1
20090247241 Gollnick et al. Oct 2009 A1
20090292905 Faraj Nov 2009 A1
20090296699 Hefty Dec 2009 A1
20090327444 Archer et al. Dec 2009 A1
20100017420 Archer et al. Jan 2010 A1
20100049836 Kramer Feb 2010 A1
20100074098 Zeng et al. Mar 2010 A1
20100095086 Eichenberger et al. Apr 2010 A1
20100185719 Howard Jul 2010 A1
20100241828 Yu et al. Sep 2010 A1
20100274876 Kagan et al. Oct 2010 A1
20100329275 Johnsen et al. Dec 2010 A1
20110060891 Jia Mar 2011 A1
20110066649 Berlyant et al. Mar 2011 A1
20110093258 Xu et al. Apr 2011 A1
20110119673 Bloch et al. May 2011 A1
20110173413 Chen et al. Jul 2011 A1
20110219208 Asaad Sep 2011 A1
20110238956 Arimilli et al. Sep 2011 A1
20110258245 Blocksome et al. Oct 2011 A1
20110276789 Chambers et al. Nov 2011 A1
20120063436 Thubert et al. Mar 2012 A1
20120117331 Krause et al. May 2012 A1
20120131309 Johnson May 2012 A1
20120254110 Takemoto Oct 2012 A1
20130117548 Grover et al. May 2013 A1
20130159410 Lee et al. Jun 2013 A1
20130159568 Shahar et al. Jun 2013 A1
20130215904 Zhou et al. Aug 2013 A1
20130250756 Johri et al. Sep 2013 A1
20130312011 Kumar et al. Nov 2013 A1
20130318525 Palanisamy et al. Nov 2013 A1
20130336292 Kore et al. Dec 2013 A1
20140019574 Cardona et al. Jan 2014 A1
20140033217 Vajda et al. Jan 2014 A1
20140040542 Kim et al. Feb 2014 A1
20140047341 Breternitz et al. Feb 2014 A1
20140095779 Forsyth et al. Apr 2014 A1
20140122831 Uliel et al. May 2014 A1
20140136811 Fleischer et al. May 2014 A1
20140189308 Hughes et al. Jul 2014 A1
20140211804 Makikeni et al. Jul 2014 A1
20140258438 Ayoub Sep 2014 A1
20140280420 Khan Sep 2014 A1
20140281370 Khan Sep 2014 A1
20140362692 Wu et al. Dec 2014 A1
20140365548 Mortensen Dec 2014 A1
20140379714 Hankins Dec 2014 A1
20150046741 Yen et al. Feb 2015 A1
20150055508 Ashida et al. Feb 2015 A1
20150074373 Sperber et al. Mar 2015 A1
20150106578 Warfield et al. Apr 2015 A1
20150143076 Khan May 2015 A1
20150143077 Khan May 2015 A1
20150143078 Khan et al. May 2015 A1
20150143079 Khan May 2015 A1
20150143085 Khan May 2015 A1
20150143086 Khan May 2015 A1
20150154058 Miwa et al. Jun 2015 A1
20150178211 Hiramoto et al. Jun 2015 A1
20150180785 Annamraju Jun 2015 A1
20150188987 Reed et al. Jul 2015 A1
20150193271 Archer et al. Jul 2015 A1
20150212972 Boettcher et al. Jul 2015 A1
20150261720 Kagan et al. Sep 2015 A1
20150269116 Raikin et al. Sep 2015 A1
20150278347 Meyer et al. Oct 2015 A1
20150318015 Bose et al. Nov 2015 A1
20150347012 Dewitt et al. Dec 2015 A1
20150365494 Cardona et al. Dec 2015 A1
20150379022 Puig et al. Dec 2015 A1
20160055225 Xu et al. Feb 2016 A1
20160092362 Barron et al. Mar 2016 A1
20160105494 Reed et al. Apr 2016 A1
20160112531 Milton et al. Apr 2016 A1
20160117277 Raindel et al. Apr 2016 A1
20160119244 Wang et al. Apr 2016 A1
20160179537 Kunzman et al. Jun 2016 A1
20160219009 French Jul 2016 A1
20160246646 Craciunas et al. Aug 2016 A1
20160248656 Anand et al. Aug 2016 A1
20160283422 Crupnicoff et al. Sep 2016 A1
20160294793 Larson et al. Oct 2016 A1
20160299872 Vaidyanathan et al. Oct 2016 A1
20160342568 Burchard et al. Nov 2016 A1
20160352598 Reinhardt et al. Dec 2016 A1
20160364350 Sanghi et al. Dec 2016 A1
20170063613 Bloch et al. Mar 2017 A1
20170093715 McGhee et al. Mar 2017 A1
20170116154 Palmer et al. Apr 2017 A1
20170187496 Shalev et al. Jun 2017 A1
20170187589 Pope et al. Jun 2017 A1
20170187629 Shalev et al. Jun 2017 A1
20170187846 Shalev et al. Jun 2017 A1
20170192782 Valentine et al. Jul 2017 A1
20170199844 Burchard et al. Jul 2017 A1
20170262517 Horowitz et al. Sep 2017 A1
20170308329 A et al. Oct 2017 A1
20170331926 Raveh et al. Nov 2017 A1
20170344589 Kafai et al. Nov 2017 A1
20180004530 Vorbach Jan 2018 A1
20180046901 Xie et al. Feb 2018 A1
20180047099 Bonig et al. Feb 2018 A1
20180089278 Bhattacharjee et al. Mar 2018 A1
20180091442 Chen et al. Mar 2018 A1
20180097721 Matsui et al. Apr 2018 A1
20180115529 Munger et al. Apr 2018 A1
20180173673 Daglis et al. Jun 2018 A1
20180262551 Demeyer et al. Sep 2018 A1
20180278549 Mula et al. Sep 2018 A1
20180285316 Thorson et al. Oct 2018 A1
20180287928 Levi et al. Oct 2018 A1
20180302324 Kasuya Oct 2018 A1
20180321912 Li et al. Nov 2018 A1
20180321938 Boswell et al. Nov 2018 A1
20180349212 Liu et al. Dec 2018 A1
20180367465 Levi Dec 2018 A1
20180375781 Chen et al. Dec 2018 A1
20190018805 Benisty Jan 2019 A1
20190026250 Das Sarma et al. Jan 2019 A1
20190044827 Ganapathi Feb 2019 A1
20190044875 Murty Feb 2019 A1
20190044889 Serres et al. Feb 2019 A1
20190056972 Zhou et al. Feb 2019 A1
20190065208 Liu et al. Feb 2019 A1
20190068501 Schneder et al. Feb 2019 A1
20190102179 Fleming et al. Apr 2019 A1
20190102338 Tang et al. Apr 2019 A1
20190102640 Balasubramanian Apr 2019 A1
20190114533 Ng et al. Apr 2019 A1
20190121388 Knowles et al. Apr 2019 A1
20190124524 Gormley Apr 2019 A1
20190138638 Pal et al. May 2019 A1
20190141133 Rajan et al. May 2019 A1
20190147092 Pal et al. May 2019 A1
20190149486 Bohrer et al. May 2019 A1
20190149488 Bansal et al. May 2019 A1
20190171612 Shahar et al. Jun 2019 A1
20190235866 Das Sarma et al. Aug 2019 A1
20190278737 Kozomora et al. Sep 2019 A1
20190303168 Fleming, Jr. et al. Oct 2019 A1
20190303263 Fleming, Jr. et al. Oct 2019 A1
20190319730 Webb et al. Oct 2019 A1
20190324431 Cella et al. Oct 2019 A1
20190339688 Cella et al. Nov 2019 A1
20190347099 Eapen et al. Nov 2019 A1
20190369994 Parandeh Afshar et al. Dec 2019 A1
20190377580 Vorbach Dec 2019 A1
20190379714 Levi et al. Dec 2019 A1
20200005859 Chen et al. Jan 2020 A1
20200034145 Bainville et al. Jan 2020 A1
20200057748 Danilak Feb 2020 A1
20200103894 Cella et al. Apr 2020 A1
20200106828 Elias et al. Apr 2020 A1
20200137013 Jin et al. Apr 2020 A1
20200202246 Lin et al. Jun 2020 A1
20200265043 Graham et al. Aug 2020 A1
20200274733 Graham et al. Aug 2020 A1
20210203621 Ylisirnio et al. Jul 2021 A1
20210218808 Graham Jul 2021 A1
20210234753 Ben-Moshe et al. Jul 2021 A1
20210243140 Levi et al. Aug 2021 A1
20210297151 Levi et al. Sep 2021 A1
20220006606 Levi et al. Jan 2022 A1
20220029854 Graham et al. Jan 2022 A1
20220188147 Nudelman et al. Jun 2022 A1
20220201103 Keppel Jun 2022 A1
Foreign Referenced Citations (1)
Number Date Country
2012216611 Mar 2013 AU
Non-Patent Literature Citations (67)
Entry
Mellanox Technologies, “InfiniScale IV: 36-port 40GB/s Infiniband Switch Device”, pp. 1-2, year 2009.
Mellanox Technologies Inc., “Scaling 10Gb/s Clustering at Wire-Speed”, pp. 1-8, year 2006.
IEEE 802.1D Standard “IEEE Standard for Local and Metropolitan Area Networks—Media Access Control (MAC) Bridges”, IEEE Computer Society, pp. 1-281, Jun. 9, 2004.
IEEE 802.1AX Standard “IEEE Standard for Local and Metropolitan Area Networks—Link Aggregation”, IEEE Computer Society, pp. 1-163, Nov. 3, 2008.
Turner et al., “Multirate Clos Networks”, IEEE Communications Magazine, pp. 1-11, Oct. 2003.
Thayer School of Engineering, “An Slightly Edited Local Copy of Elements of Lectures 4 and 5”, Dartmouth College, pp. 1-5, Jan. 15, 1998 http://people.seas.harvard.edu/˜jones/cscie129/nu_lectures/lecture11/switching/clos_network/clos_network.html.
“MPI: A Message-Passing Interface Standard,” Message Passing Interface Forum, version 3.1, pp. 1-868, Jun. 4, 2015.
Coti et al., “MPI Applications on Grids: a Topology Aware Approach,” Proceedings of the 15th International European Conference on Parallel and Distributed Computing (EuroPar'09), pp. 1-12, Aug. 2009.
Petrini et al., “The Quadrics Network (QsNet): High-Performance Clustering Technology,” Proceedings of the 9th IEEE Symposium on Hot Interconnects (Hotl'01), pp. 1-6, Aug. 2001.
Sancho et al., “Efficient Offloading of Collective Communications in Large-Scale Systems,” Proceedings of the 2007 IEEE International Conference on Cluster Computing, pp. 1-10, Sep. 17-20, 2007.
Priest et al., “You've Got Mail (YGM): Building Missing Asynchronous Communication Primitives”, IEEE International Parallel and Distributed Processing Symposium Workshops, pp. 221-230, year 2019.
InfiniBand Architecture Specification, vol. 1, Release 1.2.1, pp. 1-1727, Nov. 2007.
Deming, “Infiniband Architectural Overview”, Storage Developer Conference, pp. 1-70, year 2013.
Fugger et al., “Reconciling fault-tolerant distributed computing and systems-on-chip”, Distributed Computing, vol. 24, Issue 6, pp. 323-355, Jan. 2012.
Wikipedia, “System on a chip”, pp. 1-4, Jul. 6, 2018.
Villavieja et al., “On-chip Distributed Shared Memory”, Computer Architecture Department, pp. 1-10, Feb. 3, 2011.
Wikipedia, “Nagle's algorithm”, pp. 1-4, Dec. 12, 2019.
Bruck et al., “Efficient Algorithms for All-to-All Communications in Multiport Message-Passing Systems”, IEEE Transactions on Parallel and Distributed Systems, vol. 8, No. 11, pp. 1143-1156, Nov. 1997.
Gainaru et al., “Using InfiniBand Hardware Gather-Scatter Capabilities to Optimize MPI All-to-All”, EuroMPI '16, Edinburgh, United Kingdom, pp. 1-13, year 2016.
Pjesivac-Grbovic et al., “Performance analysis of MPI collective operations”, Cluster Computing, pp. 1-25, 2007.
Bruck et al., “Efficient Algorithms for All-to-All Communications in Multiport Message-Passing Systems”, Proceedings of the sixth annual ACM symposium on Parallel algorithms and architectures, pp. 298-309, Aug. 1, 1994.
Chiang et al., “Toward supporting data parallel programming on clusters of symmetric multiprocessors”, Proceedings International Conference on Parallel and Distributed Systems, pp. 607-614, Dec. 14, 1998.
Danalis et al., “PTG: an abstraction for unhindered parallelism”, 2014 Fourth International Workshop on Domain-Specific Languages and High-Level Frameworks for High Performance Computing, pp. 1-10, Nov. 17, 2014.
Cosnard et al., “Symbolic Scheduling of Parameterized Task Graphs on Parallel Machines,” Combinatorial Optimization book series (COOP, vol. 7), pp. 217-243, year 2000.
Jeannot et al., “Automatic Multithreaded Parallel Program Generation for Message Passing Multiprocessors using paramerized Task Graphs”, World Scientific, pp. 1-8, Jul. 23, 2001.
Stone, “An Efficient Parallel Algorithm for the Solution of a Tridiagonal Linear System of Equations,” Journal of the Association for Computing Machinery, vol. 10, No. 1, pp. 27-38, Jan. 1973.
Kogge et al., “A Parallel Algorithm for the Efficient Solution of a General Class of Recurrence Equations,” IEEE Transactions on Computers, vol. C-22, No. 8, pp. 786-793, Aug. 1973.
Hoefler et al., “Message Progression in Parallel Computing—To Thread or not to Thread?”, 2008 IEEE International Conference on Cluster Computing, pp. 1-10, Tsukuba, Japan, Sep. 29-Oct. 1, 2008.
Wikipedia, “Loop unrolling,” pp. 1-9, last edited Sep. 9, 2020 downloaded from https://en.wikipedia.org/wiki/Loop_unrolling.
Chapman et al., “Introducing OpenSHMEM: SHMEM for the PGAS Community,” Proceedings of the Forth Conferene on Partitioned Global Address Space Programming Model, pp. 1-4, Oct. 2010.
IEEE Standard 1588-2008, “IEEE Standard for a Precision Clock Synchronization Protocol for Networked Measurement and Control Systems”, pp. 1-289, year 2008.
Weibel et al., “Implementation and Performance of Time Stamping Techniques”, 2004 Conference on IEEE 1588, bages 1-29, Sep. 28, 2004.
InfiniBandTM Architecture Specification vol. 1, Release 1.2.1,pp. 1-1727, Nov. 2007.
Lu et al., “A Fast CRC Update Implementation”, Computer Engineering Laboratory, Electrical Engineering Department, pp. 113-120, Oct. 8, 2003.
Mellette et al., “Toward Optical Switching in the Data Center”, IEEE 19th International Conference on High Performance Switching and Routing (HPSR), pp. 1-6, Bucharest, Romania, Jun. 18-20, 2018.
Bakopoulos et al., “NEPHELE: an end-to-end scalable and dynamically reconfigurable optical architecture for application-aware SDN cloud datacenters”, IEEE Communications Magazine, vol. 56, issue 2, pp. 1-26, Feb. 2018.
O-RAN Alliance, “O-RAN Fronthaul Working Group; Control, User and Synchronization Plane Specification”, ORAN-WG4.CUS.0-v01.00, Technical Specification, pp. 1-189, year 2019.
Vattikonda et al., “Practical TDMA for Datacenter Ethernet”, EuroSys conference, Bern, Switzerland, pp. 225-238, Apr. 10-13, 2012.
Ericsson AB et al., “Common Public Radio Interface: eCPRI Interface Specification”, V2.0, pp. 1-109, May 10, 2019.
Xilinx Inc., “Radio over Ethernet Framer v2.1”, PB056 (v2.1), pp. 1-9, Oct. 30, 2019.
Weibel, H., “High Precision Clock Synchronization according to IEEE 1588 Implementation and Performance Issues”, Zurich University of Applied Sciences, pp. 1-9, Jan. 17, 2005.
Sanchez-Palencia, J., “[RFC,v3,net-next,00/18] Time based packet transmission”, pp. 1-14, Mar. 7, 2018.
IEEE Std 802.1Qaz™, “IEEE Standard for Local and metropolitan area networks—Media Access Control (MAC) Bridges and Virtual Bridged Local Area Networks—Amendment 18: Enhanced Transmission Selection for Bandwidth Sharing Between Traffic Classes”, IEEE Computer Society, pp. 1-110, Sep. 30, 2011.
Crupnicoff et al., “Deploying Quality of Service and Congestion Control in InfiniBand-based Data Center Networks” White Paper, Mellanox Technologies Inc, Rev 1.0, pp. 1-19, Oct. 15, 2005.
Mathey et al., “Scalable Deadlock-Free Deterministic Minimal-Path Routing Engine for InfiniBand-Based Dragonfly Networks”, IEEE Transactions on Parallel and Distributed Systems, vol. 29, No. 1, pp. 183-197, Jan. 2018.
Wikipedia, Precision Time Protocol, pp. 1-9, Apr. 20, 2020.
SMPTE Standard, “Professional Media Over Managed IP Networks:Traffic Shaping and Delivery Timing for Video”, The Society of Motion Picture and Television Engineers, pp. 1-17, Nov. 22, 2017.
Wikipedia, “Time-Sensitive Networking”, pp. 1-12, Mar. 5, 2020.
Wikipedia, “Memory Protection,” pp. 1-6, last edited May 23, 2021.
Mills, “Network Time Protocol (NTP),” RFC 0958, pp. 2-15, Sep. 1985.
Mills, “Network Time Protocol (Version 1): Specification and Implementation,” RFC 1059, pp. 2-59, Jul. 1988.
Mills, “Internet Time Synchronization: The Network Time Protocol,” IEEE Transactions on Communication, vol. 39, No. 10, pp. 1482-1493, Oct. 1991.
Mills, “Network Time Protocol (Version 3): Specification, Implementation and Analysis,” RFC 1305, pp. 1-96, Mar. 1992.
Graham, U.S. Appl. No. 18/074,563, filed Dec. 5, 2022.
EP Application #22193554.6 Search Report dated Dec. 20, 2022.
Nudelman et al., U.S. Appl. No. 18/071,692, filed Nov. 30, 2022.
Xu et al., “SLOAVx: Scalable LOgarithmic AlltoallV Algorithm for Hierarchical Multicore Systems”, 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, pp. 369-376, year 2013.
Graham, U.S. Appl. No. 63/356,923, filed Jun. 29, 2022.
U.S. Appl. No. 17/495,824 Office Action dated Jan. 27, 2023.
Yang et al., “SwitchAgg: A Further Step Toward In-Network Computing,” 2019 IEEE International Conference on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking, pp. 36-45, Dec. 2019.
Pacheco, “A User's Guide to MPI,” Department of Mathematics, University of San Francisco, CA, USA, pp. 1-51, Mar. 30, 1998.
Wikipedia, “Message Passing Interface,” pp. 1-16, last edited Nov. 7, 2021, as downloaded from https://en.wikipedia.org/wiki/Message_Passing_Interface.
“Message Passing Interface (MPI): History and Evolution,” Virtual Workshop, Cornell University Center for Advanced Computing, NY, USA, pp. 1-2, year 2021, as downloaded from https://cvw.cac.cornell.edu/mpi/history.
EP Application # 20156490.3 Office Action dated Sep. 27, 2023.
U.S. Appl. No. 17/495,824 Office Action dated Aug. 7, 2023.
U.S. Appl. No. 17/335,122 Office Action dated Jul. 28, 2023.
U.S. Appl. No. 18/071,692 Office Action dated Sep. 27, 2023.
Provisional Applications (1)
Number Date Country
63405505 Sep 2022 US