The Role of Warps in Parallel Processing

Nov 08, 2024 09:12 PM - 1 month ago 43835

Introduction

GPUs are described arsenic parallel processors for their expertise to execute activity successful parallel. Tasks are divided into smaller sub-tasks, executed simultaneously by aggregate processing units, and mixed to nutrient the last result. These processing units (threads, warps, thread blocks, cores, multiprocessors) stock resources, specified arsenic memory, facilitating collaboration betwixt them and enhancing wide GPU efficiency.

One portion successful particular, warps, are a cornerstone of parallel processing. By grouping threads together into a azygous execution unit, warps let for the simplification of thread management, the sharing of information and resources among threads, arsenic good arsenic the masking of representation latency pinch effective scheduling.

“The word warp originates from weaving, the first parallel-thread technology”

Prerequisites

It whitethorn beryllium adjuvant to publication this “CUDA refresher” earlier proceeding**

In this article, we will outline really warps are useful for optimizing the capacity of GPU-accelerated applications. By building an intuition astir warps, developers tin execute important gains successful computational velocity and efficiency.

Warps Unraveled

Thread blocks are walled into warps comprised of 32 threads each. All threads successful a warp tally connected the aforesaid Streaming Multiprocessor. Figure from an NVIDIA position connected GPGPU AND ACCELERATOR TRENDS

When a Streaming Multiprocessor (SM) is assigned thread blocks for execution, it subdivides the threads into warps. Modern GPU architectures typically person a warp size of 32 threads.

The number of warps successful a thread artifact depends connected the thread artifact size configured by the CUDA programmer. For example, if the thread artifact size is 96 threads and the warp size is 32 threads, the number of warps per thread artifact would be: 96 threads/ 32 threads per warp = 3 warps per thread block.

In this figure, 3 thread blocks are assigned to the SM. The thread blocks are comprised of 3 warps each. A warp contains 32 consecutive threads. Figure from Medium article

Note how, successful the figure, the threads are indexed, starting astatine 0 and continuing betwixt the warps successful the thread block. The first warp is made of the first 32 threads (0-31), the consequent warp has the adjacent 32 threads (32-63), and truthful forth.

Now that we’ve defined warps, let’s return a measurement backmost and look astatine Flynn’s Taxonomy, focusing connected really this categorization strategy applies to GPUs and warp-level thread management.

GPUs: SIMD aliases SIMT?

Flynn’s Taxonomy is simply a classification strategy based connected a machine architecture’s number of instructions and information streams. There are 4 classes: SISD (Single Instruction Single Data) , SIMD (Single Instruction Multiple Data), MISD (Multiple Instruction Single Data), MIMD (Multiple Instruction Multiple Data). Figure taken from CERN’s PEP root6 workshop

Flynn’s Taxonomy is simply a classification strategy based connected a machine architecture’s number of instructions and information streams. GPUs are often described arsenic Single Instruction Multiple Data (SIMD), meaning they simultaneously execute the aforesaid cognition connected aggregate information operands. Single Instruction Multiple Thread (SIMT), a word coined by NVIDIA, extends upon Flynn’s Taxonomy to amended picture the thread-level parallelism NVIDIA GPUs exhibit. In an SIMT architecture, aggregate threads rumor the aforesaid instructions to data. The mixed effort of the CUDA compiler and GPU let for threads of a warp to synchronize and execute identical instructions successful unison arsenic often arsenic possible, optimizing performance.

While some SIMD and SIMT utilization data-level parallelism, they are differentiated successful their approach. SIMD excels astatine azygous information processing, whereas SIMT offers accrued elasticity arsenic a consequence of its move thread guidance and conditional execution.

Warp Scheduling Hides Latency

In the discourse of warps, latency is the number of timepiece cycles for a warp to decorativeness executing an instruction and go disposable to process the adjacent one.

W denotes warp and T denotes thread. GPUs leverage warp scheduling to hide latency whereas CPUs execute sequentially pinch discourse switching. Figure from Lecture 6 of CalTech’s CS179

Maximum utilization is attained erstwhile each warp schedulers ever person instructions to rumor astatine each timepiece cycle. Thus, the number of resident warps, warps that are being executed connected the SM astatine a fixed moment, straight impact utilization. In different words, location needs to beryllium warps for warp schedulers to rumor instructions to. Multiple resident warps alteration the SM to move betwixt them, hiding latency and maximizing throughput.

Program Counters

Program counters increment each instruction rhythm to retrieve the programme series from memory, guiding the travel of the program’s execution. Notably, while threads successful a warp stock a communal starting programme address, they support separate programme counters, allowing for autonomous execution and branching of the individual threads.

Pre-Volta GPUs had a azygous programme antagonistic for a 32 thread warp. Following the preamble of the Volta micro-architecture, each thread has its ain programme counter. As Stephen Jones puts it during his GTC’ 17 talk : "so now each these threads are wholly independent- they still activity amended if you pack them together…but you’re nary longer dormant successful the h2o if you divided them up."Figure from Inside Volta GPUs (GTC’17).

Branching

Separate programme counters let for branching, an if-then-else programming structure, wherever instructions are processed only if threads are active. Since optimal capacity is attained erstwhile a warp’s 32 threads converge connected 1 instruction, it is advised for programmers to constitute codification that minimizes instances wherever threads wrong a warp return a divergent path.

Conclusion : Tying Up Loose Threads

Warps play an important domiciled successful GPU programming. This 32-thread portion leverages SIMT to summation the ratio of parallel processing. Effective warp scheduling hides latency and maximizes throughput, allowing for the streamlined execution of analyzable workloads. Additionally, programme counters and branching facilitate elastic thread management. Despite this flexibility, programmers are advised to debar agelong sequences of diverged execution for threads successful the aforesaid warp.