We construct programs that repeatedly run a trivial gang-scheduled computation containing a single AllReduce of a scalar followed by a scalar addition, feeding the output of one computation to the input of the next. device_set = pw.make_virtual_device_set() For 16 hosts with 128 TPUs on configuration(B), parity is reached with only 2.3ms, and even for 512 hosts with 2048 TPUs on configuration(A), a computation of at least 35ms masks all of Pathwayss single-controller overhead. Nevertheless, we believe that most of the high-level architectural choices we made in Pathways and describe in this paper would also be valid for large-scale GPU systems. understanding. Michael Isard 1 Hyeontaek Lim 1 Ruoming Pang 1 Sudip Roy 1 Brennan Saeta 1 Parker Schuh 1. Our system, Pathways, is explicitly designed to enable The tracer generates a single Pathways program where each compiled function is represented by a computation node in a dataflow graph. Isaacs, Simon Peter, Timothy Roscoe, Adrian Schpbach, and Akhilesh pipeliningHuang etal. Modeling task relationships in multi-task learning with multi-gate Hopper: Decentralized speculation-aware cluster scheduling at scale. At the same time, Pathways upends the execution model of JAX programs, pulling user code back into a single-controller model, and interposing a centralized resource management and scheduling framework between client and accelerators. On the opportunities and risks of foundation models. "Pathways: Asynchronous Distributed Dataflow for ML", Barham et al 2022 (training T5-136b on 2x1024 TPUv3-pods at 97% utilization) T, R, Code, Hardware, G Close Woosuk Kwon, Gyeong-In Yu, Eunji Jeong, and Byung-Gon Chun. Wencong Xiao, Shiru Ren, Yong Li, Yang Zhang, Pengyang Hou, Zhi Li, Yihui Feng, Peter Mattson, VijayJanapa Reddi, Christine Cheng, Cody Coleman, Greg Diamos, Since work can only be scheduled in parallel when functions are regular, Pathways treats parallel scheduling as an optimization and falls back to the traditional model when a nodes resource requirements are not known until a predecessor computation has completed (e.g.,due to data-dependent control flow). We therefore also implemented a new program tracer(Figure2) that a user can wrap around a block of Python code that calls many compiled functions. Bernauer, Bryan Catanzaro, Amar Phanishayee, and Matei Zaharia. Computations are enqueued on streams to be executed on the accelerator at some point in the future. return device_set.add_slice(tpu_devices=n).tpus, a = jax.pmap(lambda x: x * 2., devices=get_devices(2)) Configuration(A) has 4 TPUs per host, and the largest instance we report on has 512 hosts, resulting in 2048 total TPUs connected via ICI. We validate in Figure8 (performed on configuration(B)) that Pathways is able to time-multiplex accelerators between concurrent programs. Please notify us if you found a problem with this document: 1 PATHWAYS : A SYNCHRONOUS D ISTRIBUTED Dataflow FOR ML. [2021]. Yang You, Igor Gitman, and Boris Ginsburg. Very happy to see the Pathways paper that I had the great opportunity to work on finally published. then packaged and distributed to other locations. (2020); Yu and Chowdhury (2020); Wang etal. (2019), JAXBradbury etal. For example, hosts for the duration of the program execution. run the same computation in lockstep and communication Such constraints are further driving researchers towards between accelerators is described by collectives like AllRe- multiple program multiple data (MPMD) computations duce. Fused(-F): The user code contains a series of calls each of which executes a single computation node, where the node contains a chain of 128 computations. An academic search engine that utilizes artificial intelligence methods to provide highly relevant results and novel tools to filter them with ease. Our ML research colleagues have told us that they would like to use sparsity more effectively when training ever larger models, with ever more tasks, but that current frameworks limit their ability to experiment with novel model architectures. Our initial resource manager implementation uses a simple heuristic that attempts to statically balance load by spreading computations across all available devices, and keeps a one to one mapping between virtual and physical devices. [2021]. This design, with careful. Whitney Zhao, Serhat Yilmaz, Changkyu Kim, Hector Yuen, Mustafa Ozdal, etal. PATHWAYS uses a client-server architecture is a poor match for modern ML workloads that architecture that enables PATHWAYS's runtime to execute use pipelining or computational sparsity. GPU systems tend to have small islands of NVLink-connected devices (e.g., 8 GPUs within one host), with larger aggregations connected over infiniband or data-center networking technology. Ion Stoica. (Left) Distributed computation expressed as a DAG where each node represents an individual compiled function, and edges between nodes represent data flows between functions. Zorua: A holistic approach to resource virtualization in GPUs. (2018) and TensorFlow APIs. The use of TPU instead of GPU affects many of our low-level design decisions. Graph neural network training. We present both micro-benchmarks and end-to-end evaluations using real ML models that demonstrate we have met the goal of matching the performance of state-of-the-art multi-controllers for realistic workloads (5), and Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, JaredD Kaplan, Prafulla Pathways makes use of a novel asynchronous distributed dataflow design that lets the control plane execute in parallel despite dependencies in the data plane. Mesh-TensorFlow: Deep learning for supercomputers. HyoukJoong Lee, Jiquan Ngiam, QuocV Le, Yonghui Wu, etal. Clipper: A low-latency online prediction serving system. Zico: Efficient GPU memory sharing for concurrent DNN training. We have ensured strict compatibility with multi-controller JAX, and as we demonstrate in 5, Pathways matches JAXs performance across very large system scales, for all but the smallest computations. islands of accelerators connected over a data center network. We compare three ways that the user code can enqueue the For an cluster prioritizing ML training workloads, where throughput is more important than latency, it is more efficient to dedicate an entire GPU, or a static fraction of a GPU, to a single carefully sized computation at a time, than to allow the GPU driver and hardware runtime to dynamically multiplex its computational resources across competing concurrent computations. While modern GPUs support unified memorya capability to transparently page memory between accelerators, or from HBM to the hosts DRAMif the user is not careful, an HBM-bandwidth bound computation could slow to PCIe bandwidth, dropping accelerator utilization by an order of magnitudeLim etal. computational sparsity that is most naturally expressed us- This rapid recent progress of machine learning (ML) has ing fine-grain control flow and heterogeneous computation been characterized by the co-evolution of ML models, ac- across accelerators. In par-ticular, it is shown that when the proposed asynchronous 1This problem (sometimes called "the curse of the last re- Fused means executing a single actor method which runs a chain of PyTorch AllReduce commands in a loop. 1. Jonathan Heek, Anselm Levskaya, Avital Oliver, Marvin Ritter, Bertrand The constraints on compiled functions are mostly due to the co-evolution of ML models with hardware, discussed in detail in A. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory MPMD SPMD . JAXs philosophy of supporting transforms of traced code is a good match for the research directions we want to explore. Allocates n virtual TPU devices on an island. Innovative Computing Laboratory University of Tennessee Suite 203 Claxton 1122 Volunteer Blvd Knoxville, TN 37996 P: (865) 974-8295 F: (865) 974-8296. Nimble: Lightweight and parallel GPU task scheduling for deep Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, We compare the pipelined models performance to an equivalent model expressed using SPMD, and observe that at least in this instance, the pipeline has competitive performance to SPMD, since collective communication within the SPMD computation incurs higher overhead than pipeline bubble overhead. and scale-out systems. z = a(c(x)) Thus, Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban with hosts connected via DCN and scheduled using Amazon placement groups. Scalable second order optimization for deep learning. Thereska. Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen (2021). (2018); Ren etal. To increase utilization, some ML hardware resource management researchersXiao etal. We refer readers to AppendixA for a discussion on some of these properties and how they typically influence distributed ML systems. Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and (2013) to detect when all messages for a shard have been received. (Middle) Resource Manager allocates subsets of an islands accelerators (virtual slices) for each compiled function. The performance of Ray and Pathways are not directly comparable since they use different hardware, but we interpret the results to suggest that, if the full Pathways design were implemented substituting Ray for Plaque, it should be possible to achieve comparable performance. for example, the scheduler can enforce proportional share in this multi-tenancy setting. Lyndon Clarke, Ian Glendinning, and Rolf Hempel. Zero-shot learning is when a machine is taught how to learn from data without ever needing to access the data itself, while few-shot learning is when a machine is taught how to integrate data for learning from a specific point of view [6]. Deepak Narayanan, Aaron Harlap, Amar Phanishayee, Vivek Seshadri, Nikhil For this experiment, we use a model expressed in Python using TF. implementation. With careful attention to engineering, it might be possible to add fast paths to Ray, such as an on-GPU object store and primitives to transfer objects efficiently over the GPU interconnect, that eliminate most of its additional overheads. Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency This section expands on related research that addresses ML workloads that need capabilities beyond those offered by SPMD multi-controllers, and validates our Pathways design choices. Examples: NSF Implementation 10 (True) Distributed Systems tightly-coupled software on loosely-coupled hardware provide , System, Implementation, Distributed, Distributed system. The resource management and scheduling layer permits the reintroduction of cluster management policies including multi-tenant sharing, virtualization and elasticity, all tailored to the requirements of ML workloads and accelerators. The parallelism within these neural networks is amenable to sharding across multiple accelerators simultaneously, however high speed interconnects between accelerators then become critical for performance. Almost all of todays high performance ML computations are expressed as long stretches of compiled functions and only occasionally (if ever) branch based on data that is computed by a compiled function. Balancing efficiency and fairness in heterogeneous GPU clusters for (2019) and run the experiments on TPUv3s with 16GB memory per accelerator. For Pathways, OpByOp and Fused use the same JAX source as for the multi-controller, and Chained uses the Pathways program tracer to form a multi-node program where each node contains a simple computation. (2022) multiplex hardware in a fine-grained manner between workloads, enabling workload elasticity, and improving fault tolerance. design, with careful engineering, allows Pathways to adopt a single-controller Comparison of dispatch overheads and communication patterns between multi-controller and single-controller systems. TPUs are restricted to run a single program at a time, with no local pre-emption, mostly because their high-performance RDMA communication implementation between devices makes safe pre-emption difficult without distributed coordination. We could eliminate most of this overhead by allowing user code to proceed in parallel with the enqueue RPC, and opportunistically batching multiple small computations into a single Pathways program. Client programs can hold references to objects in remote host or accelerator memory, and the client and servers refer to them using opaque handles that allow the system to migrate them if needed. JAX users can explicitly wrap standard Python code Fine-grained GPU sharing primitives for deep learning applications. TPUs have a custom mesh network built directly into the chips, and chips can communicate directly without involving the host or the data-center network. (2019); Narayanan etal. (2019). Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping x = a(v) Pathwayss training throughput increases proportionally with the number of TPU cores per pipeline stage (Table2), learning to schedule language-specific capacity for This over-specialization makes it. We have implemented support to target Pathways from source programs written in TensorFlow and JAX, but we concentrate on JAX for the evaluation in this paper. Any communication beyond standard collectives in multi-controller systems requires users to implement their own coordination primitives. (2022); Weng etal. Here, we focus on how tolerance. return (y, z), print(f(numpy.array([1., 2.]))) Agarwal, Asim Shankar, Igor Ganichev, Josh Levenberg, Mingsheng Hong, Rajat TVM: An automated end-to-end optimizing compiler for deep learning. performance. It is the subject of future work to support data-dependent vectorized control flow with both a clean programming model and good performance. Examples of this architecture include MPIClarke etal. Pathways: Asynchronous Distributed . TPUs are a good fit for Pathways because XLA can compile high performance functions containing fused collectives, and the large islands of high-performance TPU interconnects allow flexible scheduling of computations of many different sizes. We present the design of a new large scale orchestration layer for accelerators. Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and EdH Chi. [1P001] Contingent affective capture: manipulating top-down search goals induces involuntary capture by threat [1P002] Can attentional templates operate in a spatially-localised f Very large language mod- els have been scaled up using pipelining rather than pure Deep learning has seen remarkable achievements over data-parallelism (Narayanan et al., 2019; Rasley et al., 2020;. (2018), but resources are still exclusively dedicated to single jobs at long time scales (seconds or more). Further, preemption of accelerator resources is minimized in practice, resulting in sub-optimal resource scheduling in large, shared clusters serving heterogeneous workloads; it is difficult to allocate large quantities of physically proximate devices to take advantage of network locality. Devanur, Greg Granger, Phil Gibbons, and Matei Zaharia. For example, JAX has a companion library called FLAXHeek etal. Analysis of large-scale multi-tenant GPU clusters for DNN The client then constructs a device location-agnostic Pathways intermediate representation (IR) for the program, expressed as a custom MLIRLattner etal. Training or inference over shared 2018; Agrawal et al., 2019). Hardware acceleration is critical to modern deep learning; unfortunately, achieving high performance with accelerators is a non-trivial systems exercise. Pathways builds extensively on prior systems, including XLATensorFlow (2019) to represent and execute TPU computations, TensorFlow graphs and executorsAbadi etal. The latency between one node completing and the next node starting can be made to be little more than the data transfer time. Pathways: Asynchronous Distributed Dataflow for ML . Our system, Pathways, is explicitly designed to enable exploration of new systems and ML research ideas, while retaining state of the art performance for current models. The trace highlights the relatively small overhead of cross-island transfer using DCN.
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