multicore

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multicore

A computer chip that contains two or more CPU processing units. Instead of continuing to squeeze more speed out of a single processor, it became obvious years ago that creating chips with multiple processing cores was the more practical approach. All major operating systems were updated to support this "symmetric multiprocessing" (SMP) capability, which enables, for example, the OS to run in one core, while each application runs in another. Antivirus running in the background can occupy its own core, and multicore architecture is ideal for virtualized servers (see virtual machine). Applications requiring extensive computations on separate sets of data, such as video encoding and 3D rendering, can also take advantage of multicore processing.

From Two to Dozens of Cores
Starting in 2005, dual-core and quad-core chips began to emerge for the x86 platform, and in subsequent years, multicore PC and Mac laptop and desktop computers became commonplace. In 2014, IBM's POWER8 CPU was introduced with up to 12 cores, and Intel introduced an 18-core Xeon chip. Other companies have developed CPUs for multimedia and scientific processing with dozens of cores. See SMP, dual core, triple core, quad core, octa-core, multithreading, multiprocessing and Cell chip.


Eight-Core POWER Chip
In 2010, IBM debuted its POWER7 generation with up to eight cores. Providing four threads per core, a 32-chip POWER7 computer can process 1,024 simultaneous instruction streams. (Image courtesy of IBM.)







Seventy-Two Cores
EZchip's Gx8072 system-on-chip (SoC) has 24 lanes of PCI Express and up to 100 Gbps of Ethernet I/O. (Image courtesy of EZchip Semiconductor Corporation, www.tilera.com)
References in periodicals archive ?
OEMs and developers can use any Matrox frame grabbers to interface to any camera type, and use multicore CPU, GPU, and FPGA technology for intensive image capture and processing tasks.
The SoCs integrate the company's most advanced image signal processor (ISP) to date and the Qualcomm Artificial Intelligence (AI) Engine, along with a heterogeneous compute architecture including a cutting-edge ARM-based multicore CPU, vector processor and GPU, it said.
The SoCs integrate Qualcomm Technologies' most advanced image signal processor (ISP) to date and the QualcommA Artificial Intelligence (AI) Engine, along with a heterogeneous compute architecture including a cutting-edge ARM-based multicore CPU, vector processor and GPU.
The SoCs integrate Qualcomm Technologies' most advanced image signal processor (ISP) to date and the Qualcomm Artificial Intelligence (AI) Engine, along with a heterogeneous compute architecture including a cutting-edge ARM-based multicore CPU, vector processor and GPU.
The SoCs integrate Qualcomm Technologies' most advanced image signal processor to date and the Qualcomm Artificial Intelligence Engine, along with a heterogeneous compute architecture including a cutting-edge ARM-based multicore CPU, vector processor and GPU.
This section describes a design and preliminary implementation of FAS on NetMagic-Pro (NMP), which is an FPGA-based network processing platform with multicore CPU.
In order to demonstrate the performance improvements between the parallel implementations on multicore CPU platform and our considered GPU platform, a multicore implementation of RLS-OPB (RLS-OPB-M) has been developed following the design principles in [10] and using OpenMP Application Program Interface (API), which is adopted to explicitly address multithreaded and shared-memory parallelism.
FPGA GPU DE5 395-D8 GTX680 GTX750Ti Number of cores1 -- -- 1152 1024 Core clock frequency (MHz) 260 193 980 1127 Memory bandwidth (GB/s) 25.6 34.1 192.2 86.4 Peak performance (Gflop/s) 193 1502.9 3090 1305 Processing time (s) 6.31 7.36 9.39 10.71 Multicore CPU i7-4960x E5-1650 v3 Number of cores1 6 6 Core clock frequency (MHz) 3600 3500 Memory bandwidth (GB/s) 51.2 59.7 Peak performance (Gflop/s) 345.6 672 Processing time (s) 23.63 20.84 (1) GPU: CUDA cores; multicore CPU: CPU cores.
Barlas presents students, academics, researchers, and professionals working in a variety of contexts with an examination of multicore CPU programming and massively parallel of many-core GPU computing.
In order to make full use of a multicore CPU, tasks should be properly generated and assigned to CPU cores [16].
For the purpose of fair comparisons, we measure the performance provided by GPU compared to the MPI code running on multicore CPU [31].
In Section 3, the DGF implementations are presented for: (i) the multicore CPU, (ii) the GPU, and (iii) the hybrid CPU-GPU parallel processing system.

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