Multithreading

About: Multithreading is a research topic. Over the lifetime, 3618 publications have been published within this topic receiving 81103 citations.

In-Datacenter Performance Analysis of a Tensor Processing Unit

Abstract: Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU)---deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, ...) that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X - 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.

Contiki - a lightweight and flexible operating system for tiny networked sensors

Abstract: Wireless sensor networks are composed of large numbers of tiny networked devices that communicate untethered. For large scale networks, it is important to be able to download code into the network dynamically. We present Contiki, a lightweight operating system with support for dynamic loading and replacement of individual programs and services. Contiki is built around an event-driven kernel but provides optional preemptive multithreading that can be applied to individual processes. We show that dynamic loading and unloading is feasible in a resource constrained environment, while keeping the base system lightweight and compact.

Simultaneous multithreading: maximizing on-chip parallelism

Abstract: This paper examines simultaneous multithreading, a technique permitting several independent threads to issue instructions to a superscalar's multiple functional units in a single cycle. We present several models of simultaneous multithreading and compare them with alternative organizations: a wide superscalar, a fine-grain multithreaded processor, and single-chip, multiple-issue multiprocessing architectures. Our results show that both (single-threaded) superscalar and fine-grain multithreaded architectures are limited their ability to utilize the resources of a wide-issue processor. Simultaneous multithreading has the potential to achieve 4 times the throughput of a superscalar, and double that of fine-grain multithreading. We evaluate several cache configurations made possible by this type of organization and evaluate tradeoffs between them. We also show that simultaneous multithreading is an attractive alternative to single-chip multiprocessors; simultaneous multithreaded processors with a variety of organizations outperform corresponding conventional multiprocessors with similar execution resources.While simultaneous multithreading has excellent potential to increase processor utilization, it can add substantial complexity to the design. We examine many of these complexities and evaluate alternative organizations in the design space.

Analyzing CUDA workloads using a detailed GPU simulator

Abstract: Modern Graphic Processing Units (GPUs) provide sufficiently flexible programming models that understanding their performance can provide insight in designing tomorrow's manycore processors, whether those are GPUs or otherwise. The combination of multiple, multithreaded, SIMD cores makes studying these GPUs useful in understanding tradeoffs among memory, data, and thread level parallelism. While modern GPUs offer orders of magnitude more raw computing power than contemporary CPUs, many important applications, even those with abundant data level parallelism, do not achieve peak performance. This paper characterizes several non-graphics applications written in NVIDIA's CUDA programming model by running them on a novel detailed microarchitecture performance simulator that runs NVIDIA's parallel thread execution (PTX) virtual instruction set. For this study, we selected twelve non-trivial CUDA applications demonstrating varying levels of performance improvement on GPU hardware (versus a CPU-only sequential version of the application). We study the performance of these applications on our GPU performance simulator with configurations comparable to contemporary high-end graphics cards. We characterize the performance impact of several microarchitecture design choices including choice of interconnect topology, use of caches, design of memory controller, parallel workload distribution mechanisms, and memory request coalescing hardware. Two observations we make are (1) that for the applications we study, performance is more sensitive to interconnect bisection bandwidth rather than latency, and (2) that, for some applications, running fewer threads concurrently than on-chip resources might otherwise allow can improve performance by reducing contention in the memory system.

Niagara: a 32-way multithreaded Sparc processor

Abstract: The Niagara processor implements a thread-rich architecture designed to provide a high-performance solution for commercial server applications. This is an entirely new implementation of the Sparc V9 architectural specification, which exploits large amounts of on-chip parallelism to provide high throughput. The hardware supports 32 threads with a memory subsystem consisting of an on-board crossbar, level-2 cache, and memory controllers for a highly integrated design that exploits the thread-level parallelism inherent to server applications, while targeting low levels of power consumption.