Data parallelism is parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different...

Flynn's taxonomy, data parallelism is usually classified as MIMD/SPMD or SIMD. Stream parallelism, also known as pipeline parallelism, focuses on dividing...

CPUs devote a lot of semiconductor area to caches and instruction-level parallelism to increase performance and to CPU modes to support operating systems...

forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing, but...

parallelism focuses on distributing tasks—concurrently performed by processes or threads—across different processors. In contrast to data parallelism...

Data parallelism features can also be implemented by libraries using dedicated data structures, such as parallel arrays. The term task parallelism is...

important new ideas behind NESL are Nested data parallelism: this feature offers the benefits of data parallelism, concise code that is easy to understand...

for the higher bandwidth of DGX (i.e., it required only data parallelism but not model parallelism). Later, it incorporated NVLinks and NCCL (Nvidia Collective...

fashion can handle the building and running of big data pipelines. Dataflow Throughput Parallelism Instruction pipeline Classic RISC pipeline Graphics...

computing. Data-parallelism applied computation independently to each data item of a set of data, which allows the degree of parallelism to be scaled with...

task, parallelism can be classified into three categories: fine-grained, medium-grained and coarse-grained parallelism. In fine-grained parallelism, a program...

The opportunity for loop-level parallelism often arises in computing programs where data is stored in random access data structures. Where a sequential...

with statically predictable access patterns are a major source of data parallelism. Dynamic arrays or growable arrays are similar to arrays but add the...

analytics engine for large-scale data processing. Spark provides an interface for programming clusters with implicit data parallelism and fault tolerance. Originally...

able to implement data parallelism, thread-level parallelism and request-level parallelism (both implementing task-level parallelism). Microarchitecture...

transformation is an algorithm that transforms nested data parallelism into flat data parallelism. It was pioneered by Guy Blelloch as part of the NESL...

on how parallelism can be expressed in order to enable more aggressive compiler optimisations. In particular, irregular nested data parallelism is not...

size N. As in this example, scalable parallelism is typically a form of data parallelism. This form of parallelism is often the target of automatic parallelization...

used SpecAugment, Stochastic Depth, and BPE Dropout. Training used data parallelism with float16, dynamic loss scaling, and activation checkpointing. Whisper...

designed off of two key observations: AI workloads exhibit substantial data parallelism, which can be mapped onto purpose built hardware, leading to performance...

Maximize parallelism, such as by splitting a single document match lookup in a large index into a MapReduce over many small indices. Partition index data and...

DirectX 11 and an open specification from Microsoft for implementing data parallelism directly in C++. It is intended to make programming GPUs easy for the...

attached to 768 hosts, connected using a combination of model and data parallelism, which was the largest TPU configuration. This allowed for efficient...

Instruction-level parallelism (ILP) is the parallel or simultaneous execution of a sequence of instructions in a computer program. More specifically,...

single program, multiple data (SPMD) is a term that has been used to refer to computational models for exploiting parallelism whereby multiple processors...

games. C++ Accelerated Massive Parallelism (C++ AMP) is a library that accelerates execution of C++ code by exploiting the data-parallel hardware on GPUs....

volumes of data. ETL applications implement three main types of parallelism: Data: By splitting a single sequential file into smaller data files to provide...

EPIC types have been in fashion. Architectures that are dealing with data parallelism include SIMD and Vectors. Some labels used to denote classes of CPU...

between intermediate results, enabling both data parallelism (across pixels, vertices etc.) and pipeline parallelism (between stages). (see also map reduce)...

key to the new Cerebras Wafer-Scale Cluster is the exclusive use of data parallelism to train, which is the preferred approach for all AI work. In November...