Distribution Vs Hardware Cores or Processors

Core Processor

Traditional microarchitectures

Shigeyuki Takano , in Thinking Machines, 2021

2.2.1 Concept of many-core

Many-core processors are used to integrate multiple processors and cache memory on a flake, a so-chosen chip multi-processor (CMP). Fig. 2.half dozen shows a many-core processor having a $\mathrm{four}\times \mathrm{iv}$ two-dimensional mesh topology. The cadre, indicated as "C" in the figure, is connected to a router, indicated as "R," through network adaptor, and the core communicates to other core(s) too as to the external world passing through the router(s) [362]. The electric current core has level-ii cache retentivity shared with the instruction and data, and level-3 tag memory to feed data from the other cores.

Figure 2.half-dozen. Many-core microprocessor.

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Deject Infrastructure Servers

Caesar Wu , Rajkumar Buyya , in Cloud Data Centers and Cost Modeling, 2015

xi.ii.1.3 Core, multicore, processor, and CPU

The terms "core," "processor," and "CPU" are very confusing due to speedily changing technology. In many circumstances, these terms become interchangeable. Traditionally, the terms "processor" or "CPU" were very straightforward and uncomplicated. They represented a microprocessor chip that implemented a series of processing tasks based on input. They executed the tasks ane by one in series. At that place was not a clear definition of processing tasks that should be included in 1 CPU.

However, when the clock speed of a microprocessor or CPU starts to hit the heat bulwark (see Effigy 1.9), microprocessor designers and engineers looked for other alternatives to increase CPU speed, and they came up with the multicore architecture or parallel computing. With a multicore architecture, hardware engineers can spring out of the "heat/performance" dilemma. In other words, the term "multicore" means multiple smaller CPUs within a large CPU.

However, how can we define the core boundary? Referring to the first commercial CPU 4004 architecture, nosotros know that it just consisted of only a few very bones execution blocks or functions, such as an arithmetic logic unit (ALU), instruction fetcher, decoder, annals, pipeline interruption handler, and I/O controller unit of measurement (see Figure xi.xiv).

Figure 11.14. The first commercial CPU: Intel 4004 [186].

Later, cache memory was added into big CPUs and the very basic or smaller execution parts of processors could be duplicated. These concrete cocky-contained execution blocks that are built along with shared enshroud memory are now called "cores."

Each core is capable of independently implementing all computational tasks without interacting with outside components (they vest to a big CPU), such as the I/O command unit, interrupt handler, etc. which are shared among all the cores.

In summary, a core is a pocket-size CPU or processor built into a large CPU or CPU socket. Information technology can independently perform or process all computational tasks. From this perspective, we can consider a core to be a smaller CPU or a smaller processor (come across Figure eleven.15) within a big processor.

Figure eleven.fifteen. Multicore processors.

Some software vendors, such as Oracle, charge license fee on a per core base rather than per socket.

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Dark Silicon and Hereafter On-scrap Systems

Pejman Lotfi-Kamran , Hamid Sarbazi-Azad , in Advances in Computers, 2018

3.one Lack of Parallelism

Moving from single-cadre processors to multicore processors was mainly motivated by the fact that many applications are inherently parallel and tin benefit from execution on multiple cores. While this intuition is by and large truthful, different applications offer dissimilar levels of parallelism. The fraction of an awarding that can be executed in parallel is referred to equally its level of parallelism. This model is very uncomplicated and assumes that parallel fraction of an application is infinitely parallelizable. With this simple model, the level of parallelism of dissimilar applications encompass a full range between 0% (not parallelizable) and 100% (fully parallelizable).

It is clear that multicore era is not useful for applications with limited parallelism. All the same, even for applications that are highly parallelizable, the serial fraction of applications (i.e., the fraction that is non parallelizable and must be run serially on a single cadre) ultimately limits the functioning that one can become using parallel execution. A simple formula captures the relationship between the level of parallelism, core count, and speedup. This formula is often referred to equally Amdahl'south constabulary [thirteen].

(4) $S=1/\left(\left(\mathrm{one}-p\right)+\left(p/n\right)\right)$

Amdahl's police force assumes that an awarding can be divided into 2 parts: serial role and parallel part. In Eq. (iv), p is the fraction of the execution time of parallel part to the whole execution time when the awarding runs on a unmarried core, n is the number of cores, and Southward is the speedup that ane gets if the application runs on n cores.

Fig. 1 shows the speedup for various numbers of cores for three applications with level of parallelism of 0.9, 0.99, and 0.999. The figure shows that the line corresponding to the application with 90% parallelism is essentially apartment. The speedup of this awarding with 1024 cores is just ten×. For an application with 99% parallelism, we detect higher speedup as compared to the application with 90% parallelism. Yet, fifty-fifty for such an application, the speedup is just 91× when the awarding executes on 1024 cores. The highest speedup is observed for an embarrassingly parallel awarding with 99.9% parallelism. The speedup for this application is significantly college than the other two applications. Fifty-fifty for such an embarrassingly parallel application, the speedup is just 506× when it runs on 1024 cores. This ways that most of the cores (i.e., 1024–506   =   518) are not utilized when such an awarding runs on 1024 cores.

Fig. 1. Speedup of three applications with 0.ix, 0.99, and 0.999 parallel fraction on various numbers of cores.

Unfortunately, commonly achieving a level of parallelism of 99% is difficult. Many parallel applications offer less than 90% parallelism. Making such applications more parallel is extremely difficult and requires enormous effort. Moreover, Fig. 1 shows that information technology is becoming more and more difficult to utilize all the cores of a multicore processor equally the number of cores increases, fifty-fifty for embarrassingly parallel applications. While there are applications with 100% parallelism (eastward.g., some data middle cloud applications similar Web Search and media streaming), many parallel applications offer parallelism beneath 100%. Fig. 1 shows that such applications are inherently incapable of utilizing all the cores of a many-core processor.

When there are only few cores available, the inherent parallelism in applications is sufficient to utilize the cores. However, when the number of cores increases, even many embarrassingly parallel applications are incapable to use all the cores. This phenomenon leads to the utilization wall in which applications cannot utilize all the cores finer. In social club to increase the utilization, we need to rewrite the applications to offer higher levels of parallelism. Unfortunately, this task is hard and time-consuming, and even sometimes incommunicable.

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Dark Silicon and Futurity On-chip Systems

Mehdi Modarressi , Hamid Sarbazi-Azad , in Advances in Computers, 2018

4 Specialized NoC for Specialized Cores

In a many-core processor in the nighttime silicon era, with a vast majority of on-chip cores left powered off (or dark), it is very likely that active cores should be noncontiguous and distributed across the network in an irregular manner.

If the NoC is designed and customized for a single application (or a group of similar applications), the cores that best match the processing requirements of each task of the application are selected (or generated) first then a mapping algorithm performs the core to NoC node mapping selectively. The major objective of well-nigh mapping algorithms is to place the cores that communicate more ofttimes (and with high volume) close to each other [65]. Nevertheless, modern full general-purpose many-core processors employ a larger number of increasingly diverse applications (often unknown at design time) with potentially unlike traffic patterns. In such systems, it is not possible to discover proper core-to-NoC mapping for all applications. For example, Fig. 1 shows the cores of a heterogeneous MPSoC that are activated to run two unlike applications. Each application activates and runs on those cores that all-time match its tasks. If the NoC was designed and customized for each individual application, the cores would be mapped into next nodes. Still, in a full general-purpose platform, it is very likely that the preferred cores should be nonadjacent. The problem in this case is that the application and the intertask traffic may non be known at design time, when the mapping is performed. Even for the target applications that are specified a priori, it is often infeasible to find a mapping that is suitable for all applications, every bit intercore traffic pattern varies significantly across applications.

Fig. one. A heterogeneous CMP with specialized cores. Active cores when playing game (A) and listening to music (B).

In this case, specialized cores for a detail application domain or course of applications (e.g., multimedia applications) are placed in a contiguous region of the flake area. However, the mapping of cores inside the region may non be optimal for all of the applications in that domain, as each application uses the cores with its own traffic pattern.

Fig. 2 shows a region of a many-cadre chip that is assigned to the cores required past a multimedia awarding set [twenty]. The application set contains H263 encoder and decoder and MP3 decoder and encoder. The mapping of cores within the region is washed by the algorithm we presented in a previous work [20] which is designed to map multiple applications with different intercore communication patterns. Every bit Fig. ii shows for ii different applications of this domain, they activate different cores of the region and nosotros withal demand topology optimization for such region-based NoCs.

Fig. 2. A region in a heterogeneous CMP with specialized cores for the MMS applications and agile cores of the region when running MP3 decoder (A) and H263 encoder (B) [10]. The communication job graph related to each awarding is likewise illustrated.

Our previous report demonstrates that for two applications, namely x and y, that apply the same set of cores merely with 50% deviation in intercore traffic, running x on an NoC whose mapping is optimized for y increases the communication latency by xxx%–55%, compared to customizing mapping for x [x].

In a partially agile CMP, conventional NoCs all the same necessitate all packets generated by active cores to get through the router pipeline in all intermediate nodes (both active and inactive) in a hop-past-hop basis. In this case, many packets may endure from long latencies, if agile nodes are located at a far topological altitude.

Due to the dynamic nature of core utilization pattern in such processors, in which the set up of active cores varies over time, a reconfigurable topology is an advisable choice for adapting to the changes in network traffic. Amidst the existing reconfigurable topologies [20–23], nosotros focus on the architecture nosotros proposed in a previous piece of work [20] (RecNoC, hereinafter), as it provides a more appropriate trade-off betwixt flexibility and area overhead than the others. As mentioned before, RecNoC relies on embedding configuration switches into a regular NoC to dynamically alter the interrouter connectivity. In this work, we show how the routers of the dark regions of a chip can be used as configuration switches and achieve the same level of power reduction and functioning improvement equally RecNoC, largely without paying its expanse overhead. The proposed NoC provides reconfigurable multihop intercore links among active cores past using the router of nighttime cores as bypass paths. This enables NoC to operate in the same manner as a customized NoC, in which the cores are placed at nearby nodes.

Obviously, with the proposed reconfiguration, the physical distance of the cores is not changed. Yet, this topology adaption machinery reduces advice power and latency significantly by reducing the number of intermediate routers. The latency and power usage of routers often dominate the total NoC latency and ability usage. For case, in Intel's 80-cadre TeraFlops, routers account for more than 80% of the NoC power consumption, whereas links consume the remaining 20% [66].

Existing NoC topologies range from regular tiled-based [5,66,67] to fully customized structures [68–lxx]. Regular NoC architectures provide standard structured interconnects that often feature high reusability and short design time/effort. On the other manus, being tailored to the traffic characteristics of one or several target applications, customized topologies offering lower latency and power consumption when running that applications. However, topology customization will transform the regular structure of standard topologies into a nonreusable advertising hoc structure with many implementation bug such equally uneven wire lengths, heterogeneous routers, and long design time. Since our proposal realizes awarding-specific customized topologies on top of structured and regular components, it stands betwixt these two extreme points of the topology design to get the all-time of both worlds; it is designed and fabricated as a regular NoC, but can exist dynamically configured to a topology that best matches the core activation design of a partially active multicore processor.

In the side by side section, we review some related research proposals on NoC power/performance optimization and and then present our night-silicon aware NoC compages.

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Motorcar learning

Jim Jeffers , ... Avinash Sodani , in Intel Xeon Phi Processor High Performance Programming (Second Edition), 2016

Threading and Work Partitioning

For a many-cadre processor like Knights Landing, exposing sufficient parallelism and partitioning work into threads becomes a crucial design task. At that place are 2 considerations for threading: (a) The work partitioning should be such that the output produced by any two threads is ideally nonoverlapping. (b) In cases where the job cannot exist partitioned into threads with nonoverlapping outputs, we privatize outputs and follow upwards the convolution layer performance with synchronization and reduce operation.

Nosotros offset examine the forward-propagation operation. Here the output activation consists of OFM*OFH*OFW neurons per image and a full of MINIBATCH* OFM*OFH*OFW neurons, each of which can exist computed independently. Hence nosotros tin can potentially have MINIBATCH*OFM*OFH*OFW (147456 work items for MINIBATCH   =   1) work items, which can be easily partitioned into hundreds of threads. However, we recall that vectorization and annals blocking require united states of america to at least have each piece of work item to have SIMD_WIDTH output feature maps, and RB_SIZE elements forth the width of the characteristic map. Hence the number of work items is reduced to (MINIBATCH*OFM*OFH*OFW)/(RB_SIZE*SIMD_WIDTH) work items. We tin simplify this further by partitioning along work items of size: SIMD_WIDTH*OFW instead of RB_SIZE*SIMD_WIDTH. For the forward-propagation, we continue to yet accept a large number of piece of work items (768 for OverFeat C5 layer for MINIBATCH   =   i), which can for example be divided into 132 threads with a load balance of 97% ((768/132)/ceiling (768/132)). While the Knights Landing preproduction function we use has 68 cores, we use just 66 of them and leave two cores for the operating system, communication direction, and I/O operations. Hence the compute is performed by 132 threads running on 66 cores.

A like work segmentation happens for the back-propagation and the threading strategy is again identical. The "gradient of inputs" (del_input) is partitioned into (IFM*IFH*IFW)/(IFW*SIMD_WIDTH) work items which are the distributed among threads.

For the weight-gradient updates, the number of piece of work items can be considerably small when the number of input and output feature maps is depression. For the OverFeat C5 layer, the number of piece of work items (recall KH   =   KW   =   3) with due consideration to the register blocking strategy leads to creation of: (OFM*IFM*KH*KW)/(KH*KW*SIMD_WIDTH*2) or 32768 work items. This yields to adept load balance. Still, for the C1 layer with OFM   =   96, IFM   =   3, KH   =   KW   =   xi, we have (OFM*IFM*KH*KW)/(SIMD_WIDTH*KW*IFM)   =   66. This can fortunately exist divided into 66 threads equally to achieve good load residual, just a slight change in topology could pb to upward-to 30% load imbalance for this layer. In such cases, we need to privatize the "gradient of weights" array and follow it up with a synchronization and reduce operation.

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Embedded Software in Real-Time Bespeak Processing Systems: Blueprint Technologies

GERT GOOSSENS , ... Fellow member, IEEE, in Readings in Hardware/Software Co-Design, 2002

A A Paradigm Shift from Hardware to Software

By increasing the amount of software in an embedded system, several of import advantages can be obtained. Starting time, it becomes possible to include late specification changes in the blueprint bike. Second, it becomes easier to differentiate an existing design, by adding new features to it. Finally, the employ of software facilitates the reuse of previously designed functions, independently from the selected implementation platform. The latter requires that functions are described at a processor-independent abstraction level (east.g., C code).

There are different types of cadre processors used in embedded systems.

•

Full general-purpose processors. Several vendors of off-the-shelf programmable processors are now offering existing processors as core components, bachelor every bit a library element in their silicon foundry [4]. Both microcontroller cores and digital bespeak processor (DSP) cores are available. From a system designer'southward point of view, general-purpose processor cores offer a quick and reliable road to embedded software, that is especially amenable to low/medium production volumes.

•

Application-specific instruction-ready processors. For high-volume consumer products, many arrangement companies adopt to blueprint an in-house application-specific instruction-set processor (ASIP) [1], [iii]. By customizing the cadre's architecture and teaching set, the arrangement'southward cost and power dissipation can be reduced significantly. The latter is crucial for portable and network-powered equipment. Furthermore, in-house processors eliminate the dependency from external processor vendors.

•

Parameterizable processors. An intermediary betwixt the previous two solutions is provided by both traditional and new "fabless" processor vendors [5]–[vii] likewise as past semiconductor departments within bigger system companies [8], [nine]. These groups are offering processor cores with a given basic architecture, but that are available in several versions, e.thou., with different register file sizes or bus widths, or with optional functional units. Designers can select the instance that all-time matches their application.

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Intel® Cadre™ Processors

In Power and Performance, 2015

3.2.1 Intel® Hard disk Graphics

Prior to the 2d Generation Intel Core processor family, Intel® integrated graphics were not part of the processor, but were instead located on the motherboard. These graphics solutions were designated Intel® Graphics Media Accelerators (Intel® GMA) and were essentially designed to accommodate users whose graphical needs weren't intensive enough to justify the expense of dedicated graphics hardware. These types of tasks included surfing the Internet or editing documents and spreadsheets. As a result, Intel GMA hardware has long been considered inadequate for graphically intensive tasks, such every bit gaming or computed-aided blueprint.

Starting with the Second Generation Intel Core processor family, Intel integrated graphics are at present role of the processor, pregnant that the GPU is now an uncore resource. While the term "integrated graphics" has typically had a negative connotation for performance, it actually has many practical advantages over discrete graphics hardware. For example, the GPU tin now share the Last Level Enshroud (LLC) with the processor's cores. Obviously, this has a drastic impact on tasks where the GPU and CPU demand to collaborate on data processing.

In many means, the GPU at present acts like another core with regards to power direction. For example, starting with the Second Generation Intel Core processor family, the GPU has a sleep state, RCvi. Boosted GPU sleep states, such as RC6P, were added in the 3rd and Fourth Generation Intel Core processors. This too means that for the processor to enter a packet C country, non only must all the CPU cores enter deep slumber, but also the GPU. Additionally, the GPU is now part of the processor's package'due south TDP, significant that the GPU can utilize the power budget of the cores and that the cores can utilize the power budget of the GPU. In other words, the GPU affects a lot of the CPU'southward power management.

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Knights Landing overview

Jim Jeffers , ... Avinash Sodani , in Intel Xeon Phi Processor High Performance Programming (Second Edition), 2016

Overview

Knights Landing is a many-cadre processor that delivers massive thread and information parallelism with high memory bandwidth. It is designed to deliver loftier performance on parallel workloads. Knights Landing provides many innovations and improvements over the prior-generation Intel Xeon Phi coprocessor code named Knights Corner. Knights Landing is a standard Intel Architecture standalone processor that can boot stock operating systems and connect to a network directly via prevalent interconnects such equally Infiniband, Ethernet, or Intel® Omni-Path Fabric ( Chapter v). This is a significant advancement over Knights Corner, which is restricted as a PCIe-connected device and, therefore, Knights Corner could just be used when connected to a separate host processor. Knights Landing introduces a more mod, ability efficient core that triples (3   ×) both scalar and vector performance compared with Knights Corner. Knights Landing offers over three TFLOP/southward of double precision and vi TFLOP/s of unmarried precision pinnacle floating point operation. It introduces a new retentivity architecture utilizing two types of memory (MCDRAM and DDR) to provide both high retention bandwidth and large retention capacity. It is binary compatible with prior Intel® processors. This means that information technology can run the aforementioned binary executable programs that run on electric current x86 or x86-64 processors without requiring any modification to the binary executable programs. Knights Landing introduces the 512-flake Avant-garde Vector Extensions known as Intel® AVX-512. These new 512-flake vector instructions are architecturally consistent with the previously introduced 256-bit AVX and AVX2 vector instructions. The AVX-512 instructions will be supported in time to come Intel® Xeon® processors also. Knights Landing introduces optional on-packet integration of the Intel® Omni-Path Architecture enabling straight network connection with improved efficiency compared to an external material chip or carte du jour.

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The Process View

Richard John Anthony , in Systems Programming, 2016

two.12.1 Questions

one.

Consider a unmarried-cadre processor system with three active processes {A, B, C}. For each of the process state configurations a-f below, place whether the combination tin can occur or not. Justify your answers.

Procedure	Process state combination
	a	b	c	d	e	f
A	Running	Ready	Running	Blocked	Ready	Blocked
B	Ready	Blocked	Running	Set	Gear up	Blocked
C	Ready	Set	Ready	Running	Ready	Blocked

two.

For a given process, which of the following state sequences (a-e) are possible? Justify your answers.

(a)

Ready   →   Running   →   Blocked   →   Set   →   Running   →   Blocked   →   Running

(b)

Prepare   →   Running   →   Fix   →   Running   →   Blocked   →   Gear up   →   Running

(c)

Gear up   →   Running   →   Blocked   →   Gear up   →   Blocked   →   Prepare   →   Running

(d)

Prepare   →   Blocked   →   Prepare   →   Running   →   Blocked   →   Running   →   Blocked

(e)

Gear up   →   Running   →   Ready   →   Running   →   Blocked   →   Running   →   Gear up

3.

Consider a compute-intensive chore that takes 100   south to run when no other compute-intensive tasks are nowadays. Calculate how long iv such tasks would take if started at the same time (state any assumptions).

four.

A non-real-time compute-intensive chore is run three times in a system, at different times of the day. The run times are 60, 61, and 80   southward, respectively. Given that the chore performs exactly the same computation each time information technology runs, how do you account for the different run times?

5.

Consider the scheduling behavior investigated in Activity P7.

(a)

What can be done to preclude real-fourth dimension tasks from having their run-fourth dimension behavior affected by groundwork workloads?

(b)

What does a existent-time scheduler need to know about processes that a general-purpose scheduler does not?

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Blueprint and Development

Colin Walls , in Embedded Software (2nd Edition), 2012

Sleep/Append

Historically, developers just had a limited range of core processor slumber states. New System-on-Chip (SoC) devices go along to expand the array of mechanisms available for a software developer to leverage in-power management. Numerous low power states (slumber, doze, hibernate, etc.) for the CPU and on-chip peripherals take become increasingly sophisticated. Coordinating the broad assortment of states across CPU and peripherals is a major job.

Low power consumption is closely related to algorithm functioning. Annihilation that tin can exist done to reduce the number of clock cycles or the clock frequency needed to execute an algorithm can be practical to reducing ability consumption. Where a platform supports sleep or various power downwardly modes, if a organization can complete the work in fewer clock cycles, it can then remain in a lower power fashion longer, thus consuming less power.

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