Heterogeneous computing refers to systems that use more than one kind of processor or cores. These systems gain performance or energy efficiency not just by adding the same type of processors, but by adding dissimilar coprocessors, usually incorporating specialized processing capabilities to handle particular tasks.
Heterogeneity
Usually heterogeneity in the context of computing referred to different instruction-set architectures, where the main processor has one and other processors have another - usually a very different - architecture, not just a different microarchitecture. For example, ARM big.LITTLE is an exception where the ISAs of cores are the same and heterogeneity refers to the speed of different microarchitectures of the same ISA, thus making it more like a symmetric multiprocessor. In the past heterogeneous computing meant different ISAs had to be handled differently, while in a modern example, Heterogeneous System Architecture systems eliminate the difference while using multiple processor types, usually on the same integrated circuit, to provide the best of both worlds: general GPU processing, while CPUs can run the operating system and perform traditional serial tasks. The level of heterogeneity in modern computing systems is gradually increasing as further scaling of fabrication technologies allows for formerly discrete components to become integrated parts of a system-on-chip, or SoC. For example, many new processors now include built-in logic for interfacing with other devices, as well as programmable functional units and hardware accelerators. Recent findings show that a heterogeneous-ISA chip multiprocessor that exploits diversity offered by multiple ISAs can outperform the best same-ISA homogeneous architecture by as much as 21% with 23% energy savings and a reduction of 32% in Energy Delay Product. AMD's 2014 announcement on its pin-compatible ARM and x86 SoCs, codename Project Skybridge, suggested a heterogeneous-ISA chip multiprocessor in the making.
Challenges
Heterogeneous computing systems present new challenges not found in typical homogeneous systems. The presence of multiple processing elements raises all of the issues involved with homogeneous parallel processing systems, while the level of heterogeneity in the system can introduce non-uniformity in system development, programming practices, and overall system capability. Areas of heterogeneity can include:
* Compute elements may interpret memory in different ways. This may include both endianness, calling convention, and memory layout, and depends on both the architecture and compiler being used.
* Library and OS services may not be uniformly available to all compute elements.
Low-Level Implementation of Language Features
* Language features such as functions and threads are often implemented using function pointers, a mechanism which requires additional translation or abstraction when used in heterogeneous environments.
* Compute elements may have different cache structures, cache coherency protocols, and memory access may be uniform or non-uniform memory access. Differences can also be found in the ability to read arbitrary data lengths as some processors/units can only perform byte-, word-, or burst accesses.
Interconnect
* Compute elements may have differing types of interconnect aside from basic memory/bus interfaces. This may include dedicated network interfaces, Direct memory access devices, mailboxes, FIFOs, and scratchpad memories, etc. Furthermore, certain portions of a heterogeneous system may be cache-coherent, whereas others may require explicit software-involvement for maintaining consistency and coherency.
Performance
* A heterogeneous system may have CPUs that are identical in terms of architecture, but have underlying micro-architectural differences that lead to various levels of performance and power consumption. Asymmetries in capabilities paired with opaque programming models and operating system abstractions can sometimes lead to performance predictability problems, especially with mixed workloads.
Data Partitioning
*While partitioning data on homogeneous platforms is often trivial, it has been shown that for the general heterogeneous case, the problem is NP-Complete. For small numbers of partitions, optimal partitionings that perfectly balance load and minimize communication volume have been shown to exist.
Example platforms
Heterogeneous computing platforms can be found in every domain of computing—from high-end servers and high-performance computing machines all the way down to low-power embedded devices including mobile phones and tablets.
