The present disclosure generally relates to the field of electronics. More particularly, an embodiment of the invention relates to techniques for scheduling applications in heterogeneous multiprocessor computing platforms.
To improve performance, some computing systems include multiple processors. However, scaling of multi-processor computing systems is restricted by power constraints. Namely, as more processors are added to a system, power consumption increases. Also, the additional power consumption generates more heat. Hence, heat and power requirements may restrict scaling of multi-processor computing systems.
The detailed description is provided with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different figures indicates similar or identical items.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of various embodiments. However, various embodiments of the invention may be practiced without the specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to obscure the particular embodiments of the invention. Further, various aspects of embodiments of the invention may be performed using various means, such as integrated semiconductor circuits (“hardware”), computer-readable instructions organized into one or more programs (“software”), or some combination of hardware and software. For the purposes of this disclosure reference to “logic” shall mean either hardware, software, or some combination thereof. Also, the use of “instruction” and “micro-operation” (uop) is interchangeable as discussed herein.
As Chip-MultiProcessor (CMP) systems become popular, e.g., for server and client platforms, heterogeneous CMP starts to gain momentum. For example, smaller processor cores may provide better performance/watt advantage. So adding smaller processor cores along with bigger processor cores can be attractive. As discussed herein, heterogeneous CMP contains a set of cores that are different in performance, area, and/or power dissipation. Such a platform provides opportunities to allow better mapping of compute resources to various application so that both performance and power efficiency may be achieved in some embodiments.
However, one of the key challenges in heterogeneous CMP platform design is application scheduling, i.e., mapping applications to the plurality of processor cores that optimizes performance and/or power efficiency. To this end, one embodiment relates to dynamically scheduling applications among heterogeneous cores (e.g., on a single integrated circuit (IC) chip/die). In one embodiment, two components may be used to schedule applications. First, a processor core modeling predication heuristics may be provided. Second, a scheduling logic may be used to schedule applications for heterogeneous processor cores based on the core modeling predication heuristics.
The techniques discussed herein may be used in any type of a processor with performance state settings, such as the processors discussed with reference to FIGS. 1 and 5-6. More particularly,
In an embodiment, the processor 102-1 may include one or more processor cores 106-1 through 106-M (referred to herein as “cores 106” or more generally as “core 106”), a shared cache 108, and/or a router 110. The processor cores 106 may be implemented on a single integrated circuit (IC) chip. Moreover, the chip may include one or more shared and/or private caches (such as cache 108), buses or interconnections (such as a bus or interconnection network 112), memory controllers (such as those discussed with reference to
In one embodiment, the router 110 may be used to communicate between various components of the processor 102-1 and/or system 100. Moreover, the processor 102-1 may include more than one router 110. Furthermore, the multitude of routers (110) may be in communication to enable data routing between various components inside or outside of the processor 102-1.
The shared cache 108 may store data (e.g., including instructions) that are utilized by one or more components of the processor 102-1, such as the cores 106. For example, the shared cache 108 may locally cache data stored in a memory 114 for faster access by components of the processor 102. In an embodiment, the cache 108 may include a mid-level cache (such as a level 2 (L2), a level 3 (L3), a level 4 (L4), or other levels of cache), a last level cache (LLC), and/or combinations thereof. Moreover, various components of the processor 102-1 may communicate with the shared cache 108 directly, through a bus (e.g., the bus 112), and/or a memory controller or hub. As shown in
In one embodiment, as will be further discussed below with reference to
As illustrated in
Further, the execution unit 208 may execute instructions out-of-order. Hence, the processor core 106 may be an out-of-order processor core in one embodiment. The core 106 may also include a retirement unit 210. The retirement unit 210 may retire executed instructions after they are committed. In an embodiment, retirement of the executed instructions may result in processor state being committed from the execution of the instructions, physical registers used by the instructions being de-allocated, etc.
The core 106 may also include a bus unit 214 to enable communication between components of the processor core 106 and other components (such as the components discussed with reference to
Moreover, in some embodiments, the logic 120 not only keeps track of performance of an application, but also predicts the application's execution and/or power consumption performance if it were to execute on another core in the system (e.g., based on the values stores in the counters 122). This information may be provided to OS which may perform scheduling based on various thresholds such as power, performance, energy, combinations thereof, etc. For example, the OS and/or logic 120 may compare the various execution or power consumption performance data of the processor cores being considered and make a determination regarding which core would provide the better execution or power consumption performance (based on various thresholds discussed herein).
In accordance with an embodiment, a signature based approach may be used. For example, each application may be executed on one or more cores in the system and the application performance statistics may be stored in the PHT a performance history table (PHT). Performance statistics may include CPI (Cycles Per Instruction), MPI (Misses Per Instruction), etc. For example, as shown in sample Table 1, each table entry may have three or more fields. The first one indicates the process ID, second is for storing the CPI of the application while executing on big core and, the last one stores the performance of the application while executing on a small core. Whenever the application is context switched to the other core, logic 120 may obtain new information and update the PHT 124.
The size of the PHT 124 may be quite small. For instance, if only CPI is used, 12 bytes per entry is the memory needed to store the history information. The PHT 124 may also be stored in the Process Control Block (PCB) of the application and/or may be loaded into another memory (e.g., PHT 124, cache 106, memory 114, cache 108, etc.) whenever the application is scheduled to run. This methodology may be extended beyond a process and may be used for various hotspots within a process.
Once the PHT is setup, every time the application is scheduled to run, the logic 120 reads the information from PHT and provides hints to OS for optimal scheduling policies based on predefined metrics (such as power/performance, etc), as shown in
Referring to
Referring to
Referring to
Referring to
In a performance counter based approach, in accordance with some embodiments, a dynamic model may be used which may effectively predict the performance of an application on a small core while it is executing on a big core and vice-versa. This approach uses the performance counters (e.g., counters 122) and predicts performance based on the following equation:
Cycles in Small core=((Cycles in Big core−stall cycles on big core)*Issue width of small core/Issue width of big core*Multiplication factor)+(L1 Miss in big core*L1 miss penalty of small core)+(L2 Miss in big core*L2 miss penalty of small core)
In an embodiment, the multiplication factor may be derived empirically based on L2 misses and number of load/store instructions. In some embodiments, the big core may have twice the number of load/store units as compared to small core. Further, in some implementations, significant L2 miss applications may not benefit by the out-of-order nature of a big core due to lack of memory level parallelism observed in some workloads.
Cycles in Big core=((Cycles in Small core−stall cycles on small core)*Issue width of Big core/Issue width of Small core)/(1−stall factor)
Stall factor may be derived by running the applications once on the big core and collecting the stall cycles and total cycles performance data. Also, some platforms may include various performance counters to identify stalls due to long latency operations such as cache miss, floating point stalls, etc. These stalls combined with other counters such as load/store instructions retired, L2 misses, etc., when used in logic 120, may help predict the performance of the application if it were to run on another core. Even if there is no specific memory stall counter in the platforms, the stalls may be estimated using other stall counters in the platform.
With respect to scheduling, some embodiments may map various applications to big and small cores based on the performance information provided by logic 120 as follows: (1) For a single application, if the performance ratio of big to small core is greater than a programmable value, then schedule the application on the big core; otherwise schedule it on the small core. This programmable value may reside in the core and may be written using MSR's (Machine State Register) based on various power/performance metrics; (2) For multiple applications, with N applications that need to be scheduled for example, order applications based on their Performance ratio of big to small core. The top N/2 apps (e.g., apps with maximal performance gains) are scheduled onto the big core, and the bottom N/2 apps (e.g., apps with the smaller performance ratios of big core to small core) are scheduled on the small core.
In some embodiments, one or more of the following counters may be used (e.g., for counters 122):
A chipset 506 may also communicate with the interconnection network 504. The chipset 506 may include a memory control hub (MCH) 508. The MCH 508 may include a memory controller 510 that communicates with a memory 512 (which may be the same or similar to the memory 114 of
The MCH 508 may also include a graphics interface 514 that communicates with a display device 516. In one embodiment of the invention, the graphics interface 514 may communicate with the display device 516 via an accelerated graphics port (AGP). In an embodiment of the invention, the display 516 (such as a flat panel display) may communicate with the graphics interface 514 through, for example, a signal converter that translates a digital representation of an image stored in a storage device such as video memory or system memory into display signals that are interpreted and displayed by the display 516. The display signals produced by the display device may pass through various control devices before being interpreted by and subsequently displayed on the display 516.
A hub interface 518 may allow the MCH 508 and an input/output control hub (ICH) 520 to communicate. The ICH 520 may provide an interface to I/O device(s) that communicate with the computing system 500. The ICH 520 may communicate with a bus 522 through a peripheral bridge (or controller) 524, such as a peripheral component interconnect (PCI) bridge, a universal serial bus (USB) controller, or other types of peripheral bridges or controllers. The bridge 524 may provide a data path between the CPU 502 and peripheral devices. Other types of topologies may be utilized. Also, multiple buses may communicate with the ICH 520, e.g., through multiple bridges or controllers. Moreover, other peripherals in communication with the ICH 520 may include, in various embodiments of the invention, integrated drive electronics (IDE) or small computer system interface (SCSI) hard drive(s), USB port(s), a keyboard, a mouse, parallel port(s), serial port(s), floppy disk drive(s), digital output support (e.g., digital video interface (DVI)), or other devices.
The bus 522 may communicate with an audio device 526, one or more disk drive(s) 528, and a network interface device 530 (which is in communication with the computer network 503). Other devices may communicate via the bus 522. Also, various components (such as the network interface device 530) may communicate with the MCH 508 in some embodiments of the invention. In addition, the processor 502 and the MCH 508 may be combined to form a single chip. Furthermore, the graphics accelerator 516 may be included within the MCH 508 in other embodiments of the invention.
Furthermore, the computing system 500 may include volatile and/or nonvolatile memory (or storage). For example, nonvolatile memory may include one or more of the following: read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically EPROM (EEPROM), a disk drive (e.g., 528), a floppy disk, a compact disk ROM (CD-ROM), a digital versatile disk (DVD), flash memory, a magneto-optical disk, or other types of nonvolatile machine-readable media that are capable of storing electronic data (e.g., including instructions).
As illustrated in
In an embodiment, the processors 602 and 604 may be one of the processors 502 discussed with reference to
At least one embodiment of the invention may be provided within the processors 602 and 604. For example, the cores 106 of
The chipset 620 may communicate with a bus 640 using a PtP interface circuit 641. The bus 640 may communicate with one or more devices, such as a bus bridge 642 and I/O devices 643. Via a bus 644, the bus bridge 642 may communicate with other devices such as a keyboard/mouse 645, communication devices 646 (such as modems, network interface devices, or other communication devices that may communicate with the computer network 503), audio I/O device 647, and/or a data storage device 648. The data storage device 648 may store code 649 that may be executed by the processors 602 and/or 604.
In various embodiments of the invention, the operations discussed herein, e.g., with reference to
Additionally, such computer-readable media may be downloaded as a computer program product, wherein the program may be transferred from a remote computer (e.g., a server) to a requesting computer (e.g., a client) by way of data signals embodied in a carrier wave or other propagation medium via a communication link (e.g., a bus, a modem, or a network connection).
Reference in the specification to “one embodiment,” “an embodiment,” or “some embodiments” means that a particular feature, structure, or characteristic described in connection with the embodiment(s) may be included in at least an implementation. The appearances of the phrase “in one embodiment” in various places in the specification may or may not be all referring to the same embodiment.
Also, in the description and claims, the terms “coupled” and “connected,” along with their derivatives, may be used. In some embodiments of the invention, “connected” may be used to indicate that two or more elements are in direct physical or electrical contact with each other. “Coupled” may mean that two or more elements are in direct physical or electrical contact. However, “coupled” may also mean that two or more elements may not be in direct contact with each other, but may still cooperate or interact with each other.
Thus, although embodiments of the invention have been described in language specific to structural features and/or methodological acts, it is to be understood that claimed subject matter may not be limited to the specific features or acts described. Rather, the specific features and acts are disclosed as sample forms of implementing the claimed subject matter.
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