Manycore processors are specialist multi-core processors designed for a high degree of parallel processing, containing numerous simpler, independent processor cores (from a few tens of cores to thousands or more). Manycore processors are used extensively in embedded computers and high-performance computing.
Contrast with multicore architecture
Manycore processors are distinct from multi-core processors in being optimized from the outset for a higher degree of explicit parallelism, and for higher throughput (or lower power consumption) at the expense of latency and lower single thread performance.
The broader category of multi-core processors, by contrast, are usually designed to efficiently run both parallel and serial code, and therefore place more emphasis on high single thread performance (e.g. devoting more silicon to out of order execution, deeper pipelines, more superscalar execution units, and larger, more general caches), and shared memory. These techniques devote runtime resources toward figuring out implicit parallelism in a single thread. They are used in systems where they have evolved continuously (with backward compatibility) from single core processors. They usually have a 'few' cores (e.g. 2,4,8), and may be complemented by a manycore accelerator (such as a GPU) in a heterogeneous system.
Cache coherency is an issue limiting the scaling of multicore processors. Manycore processors may bypass this with tricks such as message passing, scratchpad memory, DMA, partitioned global address space, or read-only/non-coherent caches. A manycore processor using a network on a chip and local memories gives software the opportunity to explicitly optimise the spatial layout of tasks (e.g. as seen in tooling developed for TrueNorth).
GPUs may be considered a form of manycore processor having multiple shader processing units, and only being suitable for highly parallel code (high throughput, but extremely poor single thread performance).
Suitable programming models
- Message passing interface
- OpenCL or other APIs supporting compute kernels
- Partitioned global address space
- Actor model
Classes of manycore systems
- GPUs, which can be described as manycore vector processors
- Massively parallel processor array
- Asynchronous array of simple processors
Specific manycore architectures
- ZettaScaler , Japanese PEZY Computing 2048-core modules
- Xeon Phi coprocessor, which has MIC (Many Integrated Cores) architecture
- Adapteva Epiphany Architecture, a manycore chip using PGAS scratchpad memory
- Coherent Logix hx3100 Processor, a 100-core DSP/GPP processor based on HyperX Architecture
- Movidius Myriad 2, a manycore Vision processing unit
- Kalray, a manycore PCI-e accelerator for data-intensive tasks
- Teraflops Research Chip a manycore processor using message passing
- TrueNorth an AI accelerator with a manycore network on a chip architecture
- Green arrays a manycore processor using message passing aimed at low power applications
- Eyeriss, a manycore processor designed for running convolutional neural nets for embedded vision applications
Specific manycore computers with 1M+ CPU cores
A number of computers built from multicore processors have one million or more individual CPU cores. Examples include:
- Sunway TaihuLight, a massively parallel (10M CPU cores) Chinese supercomputer, once one of the fastest supercomputers in the world, using a custom manycore architecture. As of November 2018, the world's third fastest supercomputer (as ranked by the TOP500 list), the Chinese Sunway TaihuLight, obtains its performance from 40,960 SW26010 manycore processors, each containing 256 cores.
- Gyoukou (Japanese: 暁光 Hepburn: gyōkō, dawn light), a supercomputer developed by ExaScaler and PEZY Computing.
- SpiNNaker, a massively parallel (1M CPU cores) manycore processor built as part of the Human Brain Project
- Vector processor
- High performance computing
- Computer cluster
- Multiprocessor system on a chip
- Vision processing unit
- Memory access pattern
- Cache coherency
- Embarrassingly parallel
- Massively parallel
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- Chen, Yu-Hsin and Krishna, Tushar and Emer, Joel and Sze, Vivienne (2016). "Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks". IEEE International Solid-State Circuits Conference, ISSCC 2016, Digest of Technical Papers. pp. 262–263.CS1 maint: uses authors parameter (link)