Modifying data streams without reordering in a multi-thread, multi-flow network processor

Abstract

Described embodiments classify packets received by a network processor. A processing module of the network processor generates tasks corresponding to each received packet. A scheduler generates contexts corresponding to tasks received by the packet classification processor from corresponding processing modules, each context corresponding to a given flow, and stores each context in a corresponding per-flow first-in, first-out buffer of the scheduler. A packet modifier generates a modified packet based on threads of instructions, each thread of instructions corresponding to a context received from the scheduler. The modified packet is generated before queuing the packet for transmission as an output packet of the network processor, and the packet modifier processes instructions for generating the modified packet in the order in which the contexts were generated for each flow, without head-of-line blocking between flows. The modified packets are queued for transmission as an output packet of the network processor.

Description

BACKGROUND

Network processors are generally used for analyzing and processing packet data for routing and switching packets in a variety of applications, such as network surveillance, video transmission, protocol conversion, voice processing, and internet traffic routing. Early types of network processors were based on software-based approaches with general-purpose processors, either singly or in a multi-core implementation, but such software-based approaches are slow. Further, increasing the number of general-purpose processors had diminishing performance improvements, or might actually slow down overall network processor throughput. Newer designs add hardware accelerators in a system on chip (SoC) architecture to offload certain tasks from the general-purpose processors, such as encryption/decryption, packet data inspections, and the like. These newer network processor designs are traditionally implemented with either i) a non-pipelined SoC architecture or ii) a fixed pipeline SoC architecture.

In a typical non-pipelined SoC architecture, general-purpose processors are responsible for each action taken by acceleration functions. A non-pipelined SoC architecture provides great flexibility in that the general-purpose processors can make decisions on a dynamic, packet-by-packet basis, thus providing data packets only to the accelerators or other processors that are required to process each packet. However, significant software overhead is involved in those cases where multiple accelerator actions might occur in sequence.

In a typical fixed-pipeline SoC architecture, packet data flows through the general-purpose processors and/or accelerators in a fixed sequence regardless of whether a particular processor or accelerator is required to process a given packet. For example, in a fixed sequence, a single accelerator within the fixed pipeline cannot be employed without employing the entire fixed pipeline. This fixed sequence might add significant overhead to packet processing and has limited flexibility to handle new protocols, limiting the advantage provided by using the accelerators.

Network processors typically employ a classification stage where each received data packet is examined to identify information about the packet, such as a source address, destination address(es) and a packet type to determine processing requirements of the packet. Based on the classification results, received packets are typically placed output queues to be scheduled for processing and transmission by the network processor based on scheduling, traffic management, or traffic shaping requirements of the network processor or destination devices in communication with the network processor.

Modification to the packet data might typically be performed subsequent to queuing It might be desirable to modify the packet data prior to the packet being placed into an output queue. Typically, packet classification and modification might be required to run simultaneously, which could slow both operations. Additionally, typical packet modifiers might be required to read and write the entire packet data. Further, if the network processor maintains packet ordering on a global basis, head of line blocking could occur where subsequent packets could not be processed until all prior (even unrelated) packets had completed processing in their entirety.

SUMMARY

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

Described embodiments provide classify packets received by a network processor. A processing module of the network processor generates tasks corresponding to each received packet. A scheduler generates contexts corresponding to tasks received by the packet classification processor from corresponding processing modules, each context corresponding to a given flow, and stores each context in a corresponding per-flow first-in, first-out buffer of the scheduler. A packet modifier generates a modified packet based on threads of instructions, each thread of instructions corresponding to a context received from the scheduler. The modified packet is generated before queuing the packet for transmission as an output packet of the network processor, and the packet modifier processes instructions for generating the modified packet in the order in which the contexts were generated for each flow, without head-of-line blocking between flows. The modified packets are queued for transmission as an output packet of the network processor.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

Other aspects, features, and advantages of described embodiments will become more fully apparent from the following detailed description, the appended claims, and the accompanying drawings in which like reference numerals identify similar or identical elements.

FIG. 1 shows a block diagram of a network processor operating in accordance with exemplary embodiments;

FIG. 2 shows a block diagram of a modular packet processor submodule of the network processor of FIG. 1 in accordance with exemplary embodiments;

FIG. 3 shows an exemplary flow diagram of packet processing by the packet processor of FIG. 2 in accordance with exemplary embodiments; and

FIG. 4 shows a block diagram of a pre-queue modifier (PQM) of the packet processor of FIG. 2 in accordance with exemplary embodiments.

DETAILED DESCRIPTION

Described embodiments classify packets received by a network processor. A processing module of the network processor generates tasks corresponding to each received packet. A scheduler generates contexts corresponding to tasks received by the packet classification processor from corresponding processing modules, each context corresponding to a given flow, and stores each context in a corresponding per-flow first-in, first-out buffer of the scheduler. A packet modifier generates a modified packet based on threads of instructions, each thread of instructions corresponding to a context received from the scheduler. The modified packet is generated before queuing the packet for transmission as an output packet of the network processor, and the packet modifier processes instructions for generating the modified packet in the order in which the contexts were generated for each flow, without head-of-line blocking between flows. The modified packets are queued for transmission as an output packet of the network processor.

Table 1 defines a list of acronyms employed throughout this specification as an aid to understanding the described embodiments:

TABLE 1
AMBA	Advanced Microcontroller Bus	AXI	Advanced eXtensible Interface
	Architecture
CAM	Content Addressable Memory	CCL	Classification Completion List
CPU	Central Processing Unit	CRC	Cyclic Redundancy Check
DDR	Double Data Rate	DMA	Direct Memory Access
DRAM	Dynamic Random Access Memory	ECC	Error Correction Code
eDRAM	Embedded DRAM	FC	Fibre Channel
FIFO	First-In, First-Out	flow ID	Flow IDentifier
FSM	Finite State Machine	I/O	Input/Output
IP	Internet Protocol	LRU	Least Recently Used
LSB	Least Significant Bit	μP	Microprocessor
MMB	Memory Manager Block	MPP	Modular Packet Processor
MTM	Modular Traffic Manager	OUL	Oldest Unspecified List
OQ	Output Queue	PAB	Packet Assembly Block
PCI-E	Peripheral Component Interconnect	PDU	Protocol Data Unit
	Express
PLB	Processor Local Bus	PIC	Packet Integrity Checker
PQM	Per-Queue Modifier	RF	Radio Frequency
SAS	Serial Attached SCSI	SATA	Serial Advanced Technology
			Attachment
SCH	Scheduler	SCSI	Small Computer System
			Interface
SED	Stream Editor	SoC	System-on-Chip
SPP	Security Protocol Processor	SRIO	Serial Rapid I/O
TCP	Transmission Control Protocol	THID	THread IDentifier
TS	Thread Start	TID	Task IDentifier
USB	Universal Serial Bus

FIG. 1 shows a block diagram of an exemplary network processor system (network processor 100) implemented as a system-on-chip (SoC). Network processor 100 might be used for processing data packets, performing protocol conversion, encrypting and decrypting data packets, or the like. As shown in FIG. 1, network processor 100 includes on-chip shared memory 112, one or more input-output (I/O) interfaces collectively shown as I/O interface 104, one or more microprocessor (μP) cores 106₁-106_M, and one or more hardware accelerators 108₁-108_N, where M and N are integers greater than or equal to 1. Network processor 100 also includes external memory interface 114 for communication with external memory 116. Shared memory 112 might be implemented as embedded dynamic random-access memory (eDRAM) internal to network processor 100. External memory 116 might typically be implemented as a double-data-rate (e.g., DDR-3) DRAM, for off-chip storage of data. In some embodiments, such as shown in FIG. 1, each of the one or more I/O interfaces, μP cores and hardware accelerators might be coupled through switch 110 to shared memory 112. Switch 110 might be implemented as a non-blocking crossbar switch such as described in related U.S. patent application Ser. No. 12/430,438 filed Apr. 27, 2009, Ser. No. 12/729,226 filed Mar. 22, 2010, and Ser. No. 12/729,231 filed Mar. 22, 2010, which are incorporated by reference herein.

I/O interface 104 might typically be implemented as hardware that connects network processor 100 to one or more external devices through I/O communication link 102. I/O communication link 102 might generally be employed for communication with one or more external devices, such as a computer system or networking device, which interface with network processor 100. I/O communication link 102 might be a custom-designed communication link, or might conform to a standard communication protocol such as, for example, a Small Computer System Interface (“SCSI”) protocol bus, a Serial Attached SCSI (“SAS”) protocol bus, a Serial Advanced Technology Attachment (“SATA”) protocol bus, a Universal Serial Bus (“USB”), an Ethernet link, an IEEE 802.11 link, an IEEE 802.15 link, an IEEE 802.16 link, a Peripheral Component Interconnect Express (“PCI-E”) link, a Serial Rapid I/O (“SRIO”) link, or any other interface link. Received packets are preferably placed in a buffer in shared memory 112 by transfer between I/O interface 104 and shared memory 112 through switch 110.

In described embodiments, shared memory 112 is a conventional memory operating as a cache that might be allocated and/or subdivided. For example, shared memory 112 might include one or more FIFO queues that might be dynamically allocated to the various μP cores 106 and hardware accelerators 108. External memory interface 114 couples shared memory 112 to one or more external memories, shown as external memory 116, to provide off-chip storage of data not currently in use by the various μP cores 106 and hardware accelerators 108 to free space in shared memory 112. As indicated by the dashed line, shared memory 112 and external memory 116 might generally be referred to as system memory 120. In general, system memory 120 might be addressed as a single address space such that various accelerators 108 can seamlessly request data whether the data is stored in shared memory 112 or external memory 116.

Hardware accelerators 108 might interact with each other, for example, by one or more communication bus rings 118 that pass “tasks” from a source core to a destination core. Tasks allow network processor 100 to process a wide variety of data and control messages more efficiently than with a fixed pipeline or non-pipelined architecture. As discussed in more detail below, the sequence of processing of the tasks depends on i) the type of packet and ii) the type of processing performed by the various cores on a particular packet (or group of packets), control message, or other data. This is referred to herein as a “Virtual Pipeline™”, a trademark of LSI Corporation, of Milpitas, Calif. In described embodiments, each of a plurality of virtual pipelines operate by each processing module of network processor 100 receiving a task, executing that task, and assigning a subsequent task to another (or the same) processing module depending on an identification of a virtual pipeline corresponding to the task. As described herein, tasks are instructions to the destination core to perform certain functions, and a task might be passed substantially as described in related U.S. patent application Ser. Nos. 12/782,379, 12/782,393, and 12/782,411 all filed May 18, 2010, which are incorporated by reference herein.

Network processor 100 might typically receive data packets from one or more source devices, perform processing operations for the received data packets, and transmit data packets out to one or more destination devices. As shown in FIG. 1, one or more data packets are transmitted from a transmitting device (not shown) to network processor 100, via I/O communication link 102. Network processor 100 might receive data packets from one or more active data streams concurrently from I/O communication link 102. I/O interface 104 might parse the received data packet and provide the received data packet, via switch 110, to a buffer in shared memory 112.

I/O interface 104 provides various types of I/O interface functions and, in exemplary embodiments described herein, is a command-driven hardware accelerator that connects network processor 100 to external devices. Received packets are preferably placed in shared memory 112 and then one or more corresponding tasks are generated. Transmitted packets are preferably generated from data in shared memory 112 for one or more corresponding tasks and might be transmitted out of network processor 100. Exemplary I/O interfaces include Ethernet I/O adapters providing integrity checks of incoming data. The I/O adapters might also provide timestamp data for received and transmitted packets that might be used to implement features such as timing over packet (e.g., specified in the standard recommendations of IEEE 1588). In alternative embodiments, I/O interface 104 might be implemented as input (receive) only or output (transmit) only interfaces.

The various μP cores 106 and hardware accelerators 108 of network processor 100 might include several exemplary types of processors or accelerators. For example, the various μP cores 106 might be implemented as Pentium®, Power PC® or ARM processors or a combination of different processor types (Pentium® is a registered trademark of Intel Corporation, ARM processors are by ARM Holdings, plc, and Power PC® is a registered trademark of IBM). The various hardware accelerators 108 might include, for example, one or more function-specific modules, such as a Modular Packet Processor (MPP), a Packet Assembly Block (PAB), a Modular Traffic Manager (MTM), a Memory Management Block (MMB), a Stream Editor (SED), a Security Protocol Processor (SPP), a Regular Expression (RegEx) engine, and other special-purpose modules.

The MTM is a software-driven accelerator that provides packet scheduling and possibly up to six levels of scheduling hierarchy. The MTM might support millions of queues and schedulers (enabling per flow queuing if desired). The MTM might provide support for shaping and scheduling with smooth deficit weighed round robin (SDWRR) for every queue and scheduler. The MTM might also support multicasting. Each copy of a packet is scheduled independently and traverses down one or more virtual pipelines enabling multicast with independent encapsulations or any other processing. The MTM might also contain a special purpose processor that can be used for fine-grained control of scheduling decisions. The MTM might be used to make discard decisions as well as scheduling and shaping decisions. The MTM might operate substantially as described in related U.S. patent application Ser. No. 13/232,422, filed Sep. 14, 2011 and Ser. No. 13/250,898 filed Sep. 30, 2011, which are incorporated by reference herein.

The SED is a software-driven accelerator that allows for editing of packets. The SED performs packet editing functions that might include adding and modifying packet headers as well as fragmenting or segmenting data (e.g., IP fragmentation). The SED receives packet data as well as parameters from tasks and a task specified per-flow state. The output of the SED can become the outgoing packet data and can update task parameters.

The RegEx engine is a packet search engine for state-based cross-packet pattern matching. The RegEx engine is a multi-threaded accelerator. An exemplary RegEx engine might be implemented such as described in U.S. Pat. No. 7,430,652 to Hundley, U.S. Pat. No. 7,899,904 to Ruehle and U.S. Pat. No. 7,512,592 to Lemoine, the teachings of which are incorporated by reference herein in their entireties.

The SPP provides encryption/decryption capabilities and is a command-driven hardware accelerator, preferably having the flexibility to handle protocol variability and changing standards with the ability to add security protocols with firmware upgrades. The ciphers and integrity (hash) functions might be implemented in hardware. The SPP has a multiple ordered task queue mechanism, discussed in more detail below, that is employed for load balancing across the threads.

The MMB allocates and frees memory resources in shared memory 112. Memory is allocated for such applications as task FIFO storage, packet data storage, hash-table collision handling, timer event management, and traffic manager queues. The MMB provides reference counts to each block of memory within shared memory 112. Multiple reference counts allow for more efficient storage of information, such as multicast traffic (data to be sent to multiple destinations) or for retransmission. Multiple reference counts remove a need for replicating data each time the data is needed. The MMB preferably tracks the memory allocations using a stack-based approach since a memory block recently released is preferably the next block to be allocated for a particular task, reducing cache thrashing and cache tracking overhead. Blocks in shared memory 112 might be dynamically allocated by the MMB to store data, with the blocks in one of the following sizes: 256, 2048, 16384, and 65536 bytes. The MMB might operate substantially as described in related U.S. patent application Ser. No. 12/963,895 filed Dec. 9, 2010 and Ser. No. 13/359,690 filed Jan. 27, 2012, the teachings of which are incorporated by reference herein.

The PAB is a command driven hardware accelerator providing a holding buffer with packet assembly, transmit, retransmit, and delete capabilities. An incoming task to the PAB can specify to insert/extract data from anywhere in any assembly buffer. Gaps are supported in any buffer. Locations to insert and extract can be specified to the bit level. Exemplary traditional packet reassembly functions might be supported, such as IP defragmentation. The PAB might also support generalized holding buffer and sliding window protocol transmit/retransmit buffering, providing an offload for features like TCP origination, termination, and normalization. The PAB might operate substantially as described in related U.S. patent application Ser. No. 12/971,742 filed Dec. 17, 2010, and Ser. No. 13/405,053 filed Feb. 23, 2012, the teachings of which are incorporated by reference herein.

The MPP is a multi-threaded special purpose processor that provides tree based longest prefix and access control list classification. The MPP also has a hardware hash-based classification capability with full hardware management of hash-table additions, deletions, and collisions. Optionally associated with each hash entry is a timer that might be used under software control for tasks such as connection timeout and retransmission timing. The MPP contains a statistics and state management engine, which when combined with the hash table and timer facilities, provides support for state-based protocol processing. The MPP might support millions of flows, limited only by the amount of DRAM capacity assigned to the functions. The MPP architecture might be able to store all per thread states in memory instead of in register files. The MPP might operate substantially as described in related U.S. patent application Ser. No. 12/974,477 filed Dec. 21, 2010, Ser. Nos. 12/975,823, 12/975,880, 12/976,045, and 12/976,228 all filed Dec. 22, 2010 and Ser. No. 13/474,114 filed May 17, 2012, the teachings of which are incorporated by reference herein. The MPP might also include hash functionality such as described in related U.S. patent application Ser. Nos. 13/046,717, 13/046,719, and 13/046,726 all filed Mar. 12, 2011, Ser. No. 13/403,468 filed Feb. 23, 2012 and Ser. No. 13/474,114 filed May 17, 2012, the teachings of which are incorporated by reference herein.

FIG. 2 shows a block diagram of an exemplary MPP 200, in accordance with described embodiments. MPP 200 might receive an input task from any μP core or accelerator (e.g., μP cores 106 or accelerators 108) of network processor 100. MPP 200 performs operations specified by the input task on a data packet stored in at least one of shared memory 112 and external memory 116. When MPP 200 is finished operating on the data packet, MPP 200 might generate an output task to another μP core or accelerator of network processor 100, for example, a next μP core or accelerator specified for a given virtual flow identifier.

As described herein, MPP 200 might generally be employed as a packet classification engine in network processor 100. In general, packet classification categorizes packets into classes, for example, based on port number or protocol. Each resulting packet class might be treated differently to control packet flow, for example, each packet class might be subject to a different rate limit or prioritized differently relative to other packet classes. Classification is achieved by various means. Matching bit patterns of data to those of known protocols is a simple, yet widely-used technique. More advanced traffic classification techniques rely on statistical analysis of attributes such as byte frequencies, packet sizes and packet inter-arrival times. Upon classifying a traffic flow using a particular protocol, a predetermined policy can be applied to it and other flows to either guarantee a certain quality (as with VoIP or media streaming service) or to provide best-effort delivery.

As shown in FIG. 2, and as will be described, packet classification might be performed by Multi-thread Instruction Engine (MTIE) 214 of MPP 200. Packet (also Protocol Data Unit or PDU) data modification might be carried out by Pre-Queue Modifier (PQM) 208. A packet integrity check might typically be carried out by Packet Integrity Checker (PIC) 210, such as determining that a packet is properly formed according to a given protocol. PIC 210 might, for example, implement various CRC and checksum functions of MPP 200. Interface to communication interface 202 might provide a standard interface between MPP 200 and chip level connections to external modules of network processor 100, for example by one or more ring communication buses. Semaphore Engine (SEM) 222 implements semaphore logic in MPP 200, to manage atomic access to a hardware resource of network processor 100 and MPP 200. For example, for a context thread to utilize an instance of a hardware resource, the context thread might have to reserve a semaphore for that resource. Hash table operations might be carried out by Hash Engine (HE) 220. HE 220 implements hash engine functionality in MPP 200. HE 220 receives instructions from Function Bus Interface (FBI) 216 over function bus 212. State Engine (SENG) 218 might perform functions of a finite state machine (FSM) that operates on received packets. For example, SENG 218 might perform statistics counts and run traffic shaper scripts.

MPP 200 might generally be implemented as a multi-threaded engine capable of executing parallel functions. The multi-threading operation is performed by multiple contexts in MTIE 214. Some embodiments of MPP 200 might employ more than one MTIE 214 to support additional context processing. For example, MPP 200 might preferably include 4 MTIE cores, each capable of processing 32 contexts, for a total of 128 contexts. These contexts might be supported by 256 task identifiers (TIDs), meaning that contexts for up to 256 tasks might be concurrently active in MPP 200.

MPP 200 might typically receive input tasks via a task ring such as described in U.S. patent application Ser. No. 12/782,379 filed May 18, 2010. Additionally, MPP 200 might receive a timer event via a timer ring. Receiving a task or receiving a timer event results in a context being generated in MPP 200 corresponding to the received task or timer event. Upon receiving a task, MPP 200 reads the task from system memory 112, for example via communication interface 202 and memory interface 224. Communication interface 202 issues a task start request to MTIE core 214 via scheduler (SCH) 204. A typical task might include 32 bytes of parameter data, and a typical timer event might include 13 bytes of parameter data.

SCH 204 tracks MPP contexts, maintains a list of free contexts, and schedules contexts for execution by the MTIE cores. SCH 204 might operate substantially as described in related U.S. patent application Ser. Nos. 12/975,823, 12/975,880, 12/976,045, 13/046,726, 12/974,477 and 12/976,228, included by reference herein. For example, upon receiving a task start request, if a free context is available, SCH 204 issues a context start indication to one or more other modules of MPP 200 such that the various modules, if necessary, might initialize themselves to process the context. Upon receiving the context start indication from SCH 204, MTIE 214 initializes its internal context memory and loads the task parameters of the received task. Upon receiving the context start indication from SCH 204, Data Buffer Controller 206 initiates a data read operation to read the packet data corresponding to the context from at least one of system memory 112 and external memory 116.

After the context start indication is issued, SCH 204 issues a context schedule indication to MTIE 214. In response to the context schedule indication, MTIE 214 starts executing a first command stored at the location specified in root address table (RAT) 228. The command might be stored in at least one of root tree memory 232, flow memory 230, and external tree memory 234. While executing the specified commands, MTIE 214 fetches tree instructions from either root tree memory 232 or external tree memory 234. MTIE 214 also fetches flow instructions from flow memory 230. Some embodiments might include a 16 KB flow memory for each MTIE core of MPP 200, and some embodiments might further allow the flow memory for multiple MTIE cores to be shared to increase the size of the flow memory for all MTIE cores.

Upon reaching a point in context processing that requires processing by a module of MPP 200 external to MTIE 214, MTIE 214 sends the context along with the corresponding function call and arguments to FBI 216. Once the context is delivered to FBI 216, the context might become inactive in MTIE 214 as, in general, a given context might only be active in one module of MPP 200 at any one time. FBI 216 provides the function call to the designated unit for execution via function bus 212. Although function bus 212 is shown in FIG. 2 as a single bus, some embodiments might employ more than one function bus 212, based on the type of module that is coupled to each bus. In general, function bus 212 might be employed to communicate between MTIE 214 and HE 220, PIC 210, SEM 222, PQM 208 and SENG 218.

Data Buffer Controller (DBC) 206 might implement the data buffer function. DBC 206 fetches PDU data for MTIE 214 from memory external to MPP 200 (e.g., one of system memory 112 or external memory 116). DBC 206 might issue a read indication signal and a read done indication signal to FBI 216 to schedule the read requests. DBC 206 might have up to 2 read requests pending at any time for a given context. FBI 216 might prevent context termination if DBC 206 has pending reads for the context.

MPP 200 might employ a flow identifier (flowID) of packets to maintain function ordering and packet scheduling. As described herein, MPP 200 might employ the flowID to determine the output packet order of network processor 100 and to order function calls and tasks within various submodules of MPP 200. As described in related U.S. patent application Ser. Nos. 12/974,477 filed on Dec. 21, 2010 and Ser. No. 13/687,719 filed on common date herewith, embodiments might typically employ an Oldest Unspecified List (OUL) to perform ordering.

MPP 200 might track a virtual flow identifier (vflow ID) and an index (vflow Index) with each output task, indicative of what one(s) of cores 106 or accelerators 108 operate on a data packet after MPP 200 has finished its processing. Communication interface 202 generates an output task based on the vflow ID and vflow Index and the output task is transmitted, for example via a task ring, to the subsequent destination module. An input task might result in the generation of multiple output tasks. As described herein, MPP 200 maintains task order between input and output, such that output tasks are generated in the order in which the input tasks are received by MPP 200, and thus also the order in which the corresponding contexts are started in MPP 200.

SCH 204 starts a new context when new tasks are received by MPP 200. SCH 204 receives a Task ID (TID) that identifies the received task and starts a context by allocating a context number to associate with that task. The TID and context number might be passed on to other modules of MPP 200 when the context is started. A context is associated with this TID and context number until SCH 204 receives an indication that processing of the context is terminated. In general, a new context is started for a received task if the following conditions are true: (1) there are available contexts; and (2) a Task Start FIFO buffer has enough available entries for at least one complete task.

Described embodiments of PQM 208 perform operations on a user-scalable number of instruction flows in parallel to reduce head-of-line blocking cases. Ordering is maintained by employing a per-context RAM and set of per-flow FIFO buffers. In described embodiments, PQM 208 maintains a list of packet modification instructions generated during packet classification by MPP 200. The list of instructions is executed on the packet data at a later time after classification is complete, rather than modifying the packet data in parallel with MPP 200 examining and classifying the packet data. Thus, MPP 200 typically reads just the packet header, rather than the entire packet, to classify the packet. When the packet modification instructions are later executed, PQM 208 fetches and modifies only the portions of the packet data that are required. This reduces the data bandwidth needed to classify packets and increases the data throughput of MPP 200. PQM 208 also supports multiple independently ordered output flows in parallel to alleviate head of line blocking.

As described in related U.S. patent application Ser. No. 12/974,477 filed on Dec. 21, 2010, in some embodiments, SCH 204 might maintain ordering of operations of PQM 208 by employing a Classification Completion List (CCL). The CCL stores pointers to the contexts and control data, such as context start order, context number, and thread identifiers (THID), for each context. When a new terminating function is issued by PQM 208 to SCH 204, the terminating function is appended to the CCL after any older CCL entries for the corresponding context. The next newest context, for example the next context in the CCL linked list, is then started. When a context becomes the oldest context in MPP 200, SCH 204 reads the CCL contents and sends them to PQM 208 to form instructions to communication interface 202 to generate a corresponding output task that is, for example, based on a vflow ID, a vflow Index, and the actual packet data. SCH 204 might determine which context is the oldest if the context is the head entry of the CCL linked list. Alternatively, if SCH 204 employs more than one output queue (OQ), a CCL linked list might exist for each output queue, and, thus, SCH 204 might select the oldest context from one of the output queues, and sends that context to PQM 208. Since an ordering requirement between OQs is not necessary, any non-empty OQ might be selected (for example, using a round robin algorithm) to begin transmission. The CCL location is freed for another context when the output task is sent to the next destination module of network processor 100.

The CCL is a linked list where newly received packets were added to the end of the CCL such that the first entry in the CCL was the oldest active packet. Thus, the CCL effectively was an ordered, linked list of instruction list pointers of PQM 208. However, a single input packet might correspond to multiple output packets (e.g., multicast packets), each output packet having associated PQM contexts in the CCL. Thus, in embodiments where the CCL is globally ordered, each output packet associated with an input packet would need to be processed before processing of a next input packet could start. Thus, if an input packet corresponded to a large number of output packets, or required extensive classification processing, head-of-line blocking could occur until classification of the first packet classification was completed, and all of its associated output packets are transmitted.

In order to support multiple flows in parallel, some embodiments might employ multiple CCLs (e.g., one CCL for each flow). However, since the CCL is based on a number of contexts of MPP 200 and given that a context might be associated with different flow IDs at different times, the size of the CCL logic might increase as the number of flows supported by MPP 200 increases. Another option could be for the CCL to include the flow identifiers corresponding to each context.

Alternatively, some embodiments might employ per-context memory 209. When PQM 208 receives a first function call for a packet, PQM 208 allocates an instruction list and sends an update message to SCH 204 that includes the context number of the packet and the head pointer to the instruction block. SCH 204 saves the head pointer in per-context memory 209 and sets a valid indicator of the head pointer. When SCH 204 determines that a context is the oldest for a particular flow ID value and the head pointer valid indicator is set, then the head pointer value for the context is read into per-flow FIFO memory 229 of SCH 204. SCH 204 employs per-flow FIFOs 229 to define the order in which the instructions for each individual flow are executed by PQM 208. For example, the instructions for each flow might desirably be executed in the same order the corresponding PDUs for the flows were started by SCH 204. SCH 204 provides an indication to PQM 208 when any per-flow FIFO is non-empty. When PQM 208 receives such an indication, PQM 208 selects a FIFO from which to select an instruction to begin output PDU modification processing.

FIG. 3 shows an exemplary flow diagram of packet process 300 of MPP 200. As shown in FIG. 3, at step 302, network processor receives a packet, and packet data is written to memory 120. At step 304, the packet is classified by MPP 200 (e.g., by classifier 402 of FIG. 4). At step 306, the packet data is modified by PQM 306, for example, as described regarding FIG. 4 by interacting with SCH 204 and building an instruction list. At step 308, the modified packet is queued for transmission as an output packet of the network processor. At step 310, the modified packet is scheduled by SCH 204 for transmission as an output packet of network processor 100. At step 312, the scheduled packet might optionally be modified by a stream editor of network processor 100 before the packet is transmitted. At step 314, the packet is transmitted as an output packet of network processor 100, and process 300 completes.

FIG. 4 shows additional detail of PQM 208. As shown in FIG. 4, PQM 208 includes per-flow state registers 414 to process instructions associated with each flow. Per-flow state registers 414 are each initialized to point to the first instruction in a corresponding instruction list stored in instruction list memory 404. Each instruction list is built by PQM 208 from instructions selected from instruction memory 406, each selected instruction corresponding to a flow context selected from per-flow FIFOs 229. As the PQM completes building an instruction list stored in 404, PQM 208 updates the per-flow state registers 414 and instruction execution module 410 begins processing any completed instruction list stored in 404. Packet modifier 412 modifies packet data retrieved from memory 112 based on executed instructions. Modified packet data is written back to memory 112. Packet data written to memory 112 might be processed or modified by other processing modules of network processor 100 (e.g., the stream editor) before network processor 100 sends the packet data as an output packet. PQM 208 might interleave modified packet data corresponding to any of the independent flows, meaning that if one flow is generating multiple output PDUs from a single input PDU, PQM 208 does not wait until all the output PDUs from a flow are processed before processing an output PDU from any other flow. In this manner progress can be made on multiple flows in parallel mitigating head-of-line blocking issues in PQM 208.

Thus, as described herein, described embodiments classify packets received by a network processor. A processing module of the network processor generates tasks corresponding to each received packet. A scheduler generates contexts corresponding to tasks received by the packet classification processor from corresponding processing modules, each context corresponding to a given flow, and stores each context in a corresponding per-flow first-in, first-out buffer of the scheduler. A packet modifier generates a modified packet based on threads of instructions, each thread of instructions corresponding to a context received from the scheduler. The modified packet is generated before queuing the packet for transmission as an output packet of the network processor, and the packet modifier processes instructions for generating the modified packet in the order in which the contexts were generated for each flow, without head-of-line blocking between flows. The modified packets are queued for transmission as an output packet of the network processor.

Reference herein to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments necessarily mutually exclusive of other embodiments. The same applies to the term “implementation.”

As used in this application, the word “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs. Rather, use of the word exemplary is intended to present concepts in a concrete fashion.

While the exemplary embodiments have been described with respect to processing blocks in a software program, including possible implementation as a digital signal processor, micro-controller, or general-purpose computer, described embodiments are not so limited. As would be apparent to one skilled in the art, various functions of software might also be implemented as processes of circuits. Such circuits might be employed in, for example, a single integrated circuit, a multi-chip module, a single card, or a multi-card circuit pack.

Described embodiments might also be embodied in the form of methods and apparatuses for practicing those methods. Described embodiments might also be embodied in the form of program code embodied in non-transitory tangible media, such as magnetic recording media, optical recording media, solid state memory, floppy diskettes, CD-ROMs, hard drives, or any other non-transitory machine-readable storage medium, wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing described embodiments. Described embodiments might can also be embodied in the form of program code, for example, whether stored in a non-transitory machine-readable storage medium, loaded into and/or executed by a machine, or transmitted over some transmission medium or carrier, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein, when the program code is loaded into and executed by a machine, such as a computer, the machine becomes an apparatus for practicing the described embodiments. When implemented on a general-purpose processor, the program code segments combine with the processor to provide a unique device that operates analogously to specific logic circuits. Described embodiments might also be embodied in the form of a bitstream or other sequence of signal values electrically or optically transmitted through a medium, stored magnetic-field variations in a magnetic recording medium, etc., generated using a method and/or an apparatus of the described embodiments.

It should be understood that the steps of the exemplary methods set forth herein are not necessarily required to be performed in the order described, and the order of the steps of such methods should be understood to be merely exemplary. Likewise, additional steps might be included in such methods, and certain steps might be omitted or combined, in methods consistent with various described embodiments.

As used herein in reference to an element and a standard, the term “compatible” means that the element communicates with other elements in a manner wholly or partially specified by the standard, and would be recognized by other elements as sufficiently capable of communicating with the other elements in the manner specified by the standard. The compatible element does not need to operate internally in a manner specified by the standard. Unless explicitly stated otherwise, each numerical value and range should be interpreted as being approximate as if the word “about” or “approximately” preceded the value of the value or range.

Also for purposes of this description, the terms “couple,” “coupling,” “coupled,” “connect,” “connecting,” or “connected” refer to any manner known in the art or later developed in which energy is allowed to be transferred between two or more elements, and the interposition of one or more additional elements is contemplated, although not required. Conversely, the terms “directly coupled,” “directly connected,” etc., imply the absence of such additional elements. Signals and corresponding nodes or ports might be referred to by the same name and are interchangeable for purposes here.

It will be further understood that various changes in the details, materials, and arrangements of the parts that have been described and illustrated in order to explain the nature of the described embodiments might be made by those skilled in the art without departing from the scope expressed in the following claims.

Claims

1. A packet classification processor for a network processor having a plurality of processing modules and at least one shared memory, wherein the network processor generates one or more tasks corresponding to each of a plurality of received packets, the packet processor comprising: a scheduler configured to generate one or more contexts corresponding to tasks received by the packet classification processor from corresponding ones of the plurality of processing modules, each context corresponding to a given flow, wherein each context is stored in a corresponding per-flow first-in, first-out buffer;a packet modifier configured to generate a modified packet based on one or more threads of instructions, each thread of instructions corresponding to a context received from the scheduler, wherein the modified packet is generated before the packet is queued for transmission as an output packet of the network processor, wherein the packet modifier is configured to process instructions for generating the modified packet in the order in which the contexts were generated for each flow, without head-of-line blocking between flows;the network processor is configured to (i) queue the modified packet in an associated output queue, (ii) schedule a queued packet for transmission as an output packet of the network processor, and (iii) transmit the scheduled packet as an output packet.

2. The packet classification processor of claim 1, further comprising: a classifier configured to classify a traffic type of each received packet.

3. The packet classification processor of claim 1, wherein the packet modifier comprises: one or more per-flow state registers configured to maintain state data for each flow, wherein the per-flow state registers point to a first instruction in a corresponding instruction list of the flow;an instruction manager configured to generate one or more instruction lists, each instruction list comprising a list of instructions for the packet modifier to process to generate a modified packet corresponding to the flow associated with the instruction list, the one or more instructions in each instruction list corresponding to instructions stored in an instruction memory of the packet modifier;an instruction execution module configured to process completed instruction lists;a packet modifying engine configured to (i) read packet data from the at least one shared memory, (ii) modify the read packet data based on the corresponding instructions executed by the instruction execution module, and (iii) write the modified packet data to the at least one shared memory.

4. The packet classification module of claim 1, wherein the modified packet data is processed by one or more of the plurality of processing modules of the network processor before the packet is transmitted as an output packet.

5. The packet classification module of claim 1, wherein the packet modifier is configured to interleave modified packet data corresponding to any of the independent flows, thereby processing multiple flows in parallel and reducing head-of-line blocking.

6. The packet classification module of claim 1, further comprising: a thread status manager configured to maintain: (i) a thread status table having N status entries, configured to track up to N active threads, where N is a positive integer, and wherein each status entry corresponds to an active thread, and each status entry comprises a valid status indicator, a sequence value, a thread indicator value, and a flow indicator value, and(ii) a sequence counter configured to generate a sequence value for each thread of each flow, wherein the sequence counter is incremented each time processing of a thread for a flow is started by the multi-thread instruction engine, and the sequence counter is decremented each time a thread for a flow is completed by the multi-thread instruction engine, whereby a lower relative sequence value indicates an earlier started thread for each flow,wherein the thread indicator value of an oldest thread of each flow of the thread status table has a corresponding sequence value equal to 0 for each flow.

7. A method of classifying packets received by a network processor having a plurality of processing modules and at least one shared system memory, the method comprising: generating, by at least one of the plurality of processing modules, one or more tasks corresponding to each of a plurality of received packets;generating, by a scheduler, one or more contexts corresponding to tasks received by the packet classification processor from corresponding ones of the plurality of processing modules, each context corresponding to a given flow, and storing each context in a corresponding per-flow first-in, first-out buffer of the scheduler;generating, by a packet modifier, a modified packet based on one or more threads of instructions, each thread of instructions corresponding to a context received from the scheduler, wherein the modified packet is generated before queuing the packet for transmission as an output packet of the network processor, wherein the packet modifier processes instructions for generating the modified packet in the order in which the contexts were generated for each flow, without head-of-line blocking between flows;queuing the modified packet in an associated output queue;scheduling a queued packet for transmission as an output packet of the network processor; andtransmitting the scheduled packet as an output packet.

8. The method of claim 7, further comprising: classifying, by a classifier of the packet classification module, a traffic type of each received packet.

9. The method of claim 7, further comprising: maintaining, by one or more per-flow state registers of the packet modifier, state data for each flow, wherein the per-flow state registers point to a first instruction in a corresponding instruction list of the flow;generating, by an instruction manager of the packet modifier, one or more instruction lists, each instruction list comprising a list of instructions for the packet modifier to process to generate a modified packet corresponding to the flow associated with the instruction list, the one or more instructions in each instruction list corresponding to instructions stored in an instruction memory of the packet modifier;processing, by an instruction execution module of the packet modifier, completed instruction lists;by a packet modifying engine of the packet modifier: reading packet data from the at least one shared memory;modifying the read packet data based on the corresponding instructions executed by the instruction execution module; andwriting the modified packet data to the at least one shared memory.

10. The method of claim 7, further comprising: processing the modified packet data by one or more of the plurality of processing modules of the network processor before transmitting the packet as an output packet of the network processor.

11. The method of claim 7, further comprising: interleaving, by the packet modifier, modified packet data corresponding to any of the independent flows, thereby processing multiple flows in parallel and reducing head-of-line blocking.

12. The method of claim 7, further comprising the steps of: allocating, by the scheduler, a corresponding one of the one or more threads for each flow to the multi-thread instruction engine for processing; anddeallocating, by the scheduler, a corresponding one of the one or more threads for each flow from the multi-thread instruction engine upon the multi-thread instruction engine completing the corresponding thread.

13. The method of claim 7, further comprising: maintaining, by a thread status manager, (i) a thread status table having N status entries, where N is a positive integer, and (ii) a sequence counter for each flow;tracking, by the thread status table, up to N active threads, wherein each status entry corresponds to an active thread, and each status entry comprises a valid status indicator, a sequence value, a thread indicator value, and a flow indicator value,generating, by each sequence counter, a sequence value for each thread by the steps of: incrementing the sequence counter each time processing of a thread for the associated flow is started by the multi-thread instruction engine, anddecrementing the sequence counter each time a thread for the associated flow is completed by the multi-thread instruction engine, whereby a lower relative sequence value indicates an earlier started thread for the flow; andproviding, by the thread status manager to the scheduler, the oldest thread for each flow of the thread status table, wherein the oldest thread has a corresponding sequence value equal to 0 for each flow.

14. A non-transitory machine-readable medium, having encoded thereon program code, wherein, when the program code is executed by a machine, the machine implements a method of classifying packets received by a network processor having a plurality of processing modules and at least one shared system memory, the method comprising: generating, by at least one of the plurality of processing modules, one or more tasks corresponding to each of a plurality of received packets;generating, by a scheduler, one or more contexts corresponding to tasks received by the packet classification processor from corresponding ones of the plurality of processing modules, each context corresponding to a given flow, and storing each context in a corresponding per-flow first-in, first-out buffer of the scheduler;generating, by a packet modifier, a modified packet based on one or more threads of instructions, each thread of instructions corresponding to a context received from the scheduler, wherein the modified packet is generated before queuing the packet for transmission as an output packet of the network processor, wherein the packet modifier processes instructions for generating the modified packet in the order in which the contexts were generated for each flow, without head-of-line blocking between flows;queuing the modified packet in an associated output queue;scheduling a queued packet for transmission as an output packet of the network processor; andtransmitting the scheduled packet as an output packet.

15. The non-transitory machine-readable medium of claim 14, further comprising: classifying, by a classifier of the packet classification module, a traffic type of each received packet.

16. The non-transitory machine-readable medium of claim 14, further comprising: maintaining, by one or more per-flow state registers of the packet modifier, state data for each flow, wherein the per-flow state registers point to a first instruction in a corresponding instruction list of the flow;generating, by an instruction manager of the packet modifier, one or more instruction lists, each instruction list comprising a list of instructions for the packet modifier to process to generate a modified packet corresponding to the flow associated with the instruction list, the one or more instructions in each instruction list corresponding to instructions stored in an instruction memory of the packet modifier;processing, by an instruction execution module of the packet modifier, completed instruction lists;by a packet modifying engine of the packet modifier: reading packet data from the at least one shared memory;modifying the read packet data based on the corresponding instructions executed by the instruction execution module; andwriting the modified packet data to the at least one shared memory.

17. The non-transitory machine-readable medium of claim 14, further comprising: processing the modified packet data by one or more of the plurality of processing modules of the network processor before transmitting the packet as an output packet of the network processor.

18. The non-transitory machine-readable medium of claim 14, further comprising: interleaving, by the packet modifier, modified packet data corresponding to any of the independent flows, thereby processing multiple flows in parallel and reducing head-of-line blocking.

19. The non-transitory machine-readable medium of claim 14, further comprising the steps of: allocating, by the scheduler, a corresponding one of the one or more threads for each flow to the multi-thread instruction engine for processing; anddeallocating, by the scheduler, a corresponding one of the one or more threads for each flow from the multi-thread instruction engine upon the multi-thread instruction engine completing the corresponding thread.

20. The non-transitory machine-readable medium of claim 14, further comprising: maintaining, by a thread status manager, (i) a thread status table having N status entries, where N is a positive integer, and (ii) a sequence counter for each flow;tracking, by the thread status table, up to N active threads, wherein each status entry corresponds to an active thread, and each status entry comprises a valid status indicator, a sequence value, a thread indicator value, and a flow indicator value,generating, by each sequence counter, a sequence value for each thread by the steps of: incrementing the sequence counter each time processing of a thread for the associated flow is started by the multi-thread instruction engine, anddecrementing the sequence counter each time a thread for the associated flow is completed by the multi-thread instruction engine, whereby a lower relative sequence value indicates an earlier started thread for the flow; andproviding, by the thread status manager to the scheduler, the oldest thread for each flow of the thread status table, wherein the oldest thread has a corresponding sequence value equal to 0 for each flow.