Xiaoqi Ren, Ganesh Ananthanarayanan, Adam Wierman, and Minlan Yu. (2019); Yang etal. It is noteworthy that Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Modern deep neural networks are orders of magnitude larger than the capacity of accelerator (HBM) memoryLepikhin etal. asynchronous operators that consume and produce futures, and efficiently (2019); Narayanan etal. 10 , user's code runs on each of the accelerator hosts and dis- 2017). multiplex hardware in a fine-grained manner between work- sion on some of these properties and how they typically loads, enabling workload elasticity, and improving fault influence Distributed ML systems. Zhihao Bai, Zhen Zhang, Yibo Zhu, and Xin Jin. Monga, etal. c = jax.pmap(lambda x: x / 2., devices=get_devices(2)), @pw.program # Program tracing (optional) Training or inference over shared sub-models can benefit from techniques that allow examples from different tasks to be combined in a single vectorized batch to get better accelerator utilization. Wanderman-Milne, and Qiao Zhang. The use of a single message is designed to minimize network traffic, but does not require the scheduler to actually enqueue all the subgraphs shards as a batch: computations may still be interleaved with those Aggregate throughput of concurrent programs (compute times in ms). The Pathways client uses a sharded buffer abstraction to represent a logical buffer that may be distributed over multiple devices. def f(v): (2021); Lim etal. Innovative Computing Laboratory University of Tennessee Suite 203 Claxton 1122 Volunteer Blvd Knoxville, TN 37996 P: (865) 974-8295 F: (865) 974-8296. Themis: Fair and efficient GPU cluster scheduling. We refer to these computations with known resource requirements as compiled functions. [2020], and use RDMA capabilities of ethernet and Infiniband NICs (GPUDirect) to rapidly communicate between the islands. Analysis, Efficient Algorithms for Device Placement of DNN Graph Operators, Model-Parallel Model Selection for Deep Learning Systems, GX-Plug: a Middleware for Plugging Accelerators to Distributed Graph Looking forward to attend the conference in August. blog: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ zhihu: https . Models like Mixture of Experts (MoE)Shazeer etal. The paper has been accepted for publication in MLSys 2022. The resource manager dynamically assigns physical devices for virtual devices satisfying the desired interconnect topology, memory capacity, etc. Pathways: Asynchronous Distributed Dataflow for ML. Ryan Sepassi 1 Laurent El Shafey 1 Chandramohan A. Thekkath 1 Yonghui Wu 1. Pathways instantiates a CPU-based TensorFlow executor on each host, so that user programs can serialize input processing into a TensorFlow graph and distribute it across the workers. First, the representation used to describe the Pathways IR must contain a single node for each sharded computation, to ensure a compact representation for computations that span many shards, i.e. Chained means chaining a sequence of actor methods (by passing Ray futures), each of which executes a single PyTorch AllReduce. When evaluating Ray on GPU we use Ray v1.3 and PyTorch 1.8.1 running on p3.2xlarge VMs111These VMs have 1V100 GPU and 8CPU cores. Secondly, to support concurrent execution of MPMD programs with SPMD sub-computations, each spanning a subset of accelerators drawn from a shared cluster, the runtime must have some mechanism to support gang-scheduling of accelerator computations. Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris (2021), a foundation model for different tasks, using the same accelerators to hold the fixed foundation model layers. Shrestha, Saugata Ghose, Adwait Jog, PhillipB Gibbons, and Onur Mutlu. needed for the research and deployment of novel and efficient ML methods. Figure10 shows a trace of a sample of cores when the stages are partitioned into islands. exploration of new systems and ML research ideas, while retaining state of the HyoukJoong Lee, Jiquan Ngiam, QuocV Le, Yonghui Wu, etal. The multikernel: A new OS architecture for scalable multicore submitted by other concurrently executing programs. A Pathways backend consists of a set of accelerators grouped into tightly-coupled islands that are in turn connected to each other over DCN (Figure3). Pathways uses a sharded dataflow graph of [2018], Paszke etal. (2016) avoids dedicating a whole accelerator to a single user. TensorFlow Eager: A multi-stage, Python-embedded DSL for Pathwayss single-controller model grants the system an extensive ability to track available resources and to allocate resources at large scale. Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, DerekG. Murray, While there are many similarities between GPUs and TPUs, there are some important differences. Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen. The system executes the entire chain The use of TPU instead of GPU affects many of our low-level design decisions. Finally, we show the performance of Pathways in training real machine learning models that can be expressed as SPMD programs. Pathways uses a client-server architecture that enables Pathwayss runtime to execute programs on system-managed islands of compute on behalf of many clients. Transformer. Eventually we expect that transfer overheads would dominate again. 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. 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. Providing exclusive access to large islands of homogeneous accelerators connected over high-bandwidth interconnects is expensive, and often wasteful as a single user program must try to keep all of the accelerators continuously busy. Large batch training of convolutional networks. The Innovus Implementation System provides new capabilities in placement, optimization, routing, and clocking. Zinenko. Pro- viding exclusive access to large islands of homogeneous For example, most of today's state-of-the-art ML workloads accelerators connected over high-bandwidth interconnects use a single program multiple data (SPMD) model, in- is expensive, and often wasteful as a single user program spired by MPI (Clarke et al.). Allocates n virtual TPU devices on an island. some of the design and implementation choices of existing Distributed ML systems make it hard for them to support Finally, researchers are beginning to standardize on a set large, sparse or irregular models. (2020); Yu and Chowdhury (2020); Wang etal. Monitors in a splitkernel can be heterogeneous and can be added, removed, and restarted dynamically without affecting the rest of the system. Wencong Xiao, Shiru Ren, Yong Li, Yang Zhang, Pengyang Hou, Zhi Li, Yihui Feng, (2021); Zhao etal. We use model configurations fromRaffel etal. and Christopher DeSa. Ray PLAQUE , scheduler island programs , ABC 3 DCN message, host Ahost Bhost C A host A A host B host B B host A B A B host B B , a, b"", host a sharded object store Ray object stores, 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. actor actor , object stores program client computation. We demonstrate that Pathways can achieve performance parity (~100% accelerator utilization) with state-of-the-art systems when running SPMD computations over 2048 TPUs, while also delivering throughput comparable to the SPMD case for Transformer models that are pipelined across 16 stages, or sharded across two islands of accelerators connected over a data center network. Host A enqueues node A, receives a future for As outputs, and transmits the future to host B. 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. Recently, researchers have begun to run into the limits of SPMD for ML computations. Gandhi, Adwait Jog, ChristopherJ Rossbach, and Onur Mutlu. Megatron-LM. JAX has deliberately avoided re-implementing data loading pipelines, and tensorflow/datasetsTensorFlow [2021] are commonly used for JAX input processing, so it is not difficult for JAX programs to be adapted to offload input processing to the CPU-based TensorFlow executors run on Pathways workers. networks with Roc. program a, b, c, a computation computation SPMD . DCN transfers occur between every group of 8 rows in the trace, and are not visible in the trace because communication time is effectively overlapped with computation. The variability and nondispatchability of todays PV systems affect the stability of the utility grid and the economics of the PV and energy distribution systems. Yang You, Igor Gitman, and Boris Ginsburg. Bowen Yang, Jian Zhang, Jonathan Li, Christopher R, Christopher Aberger, Out of the box, Ray shows about an order of magnitude worse performance per computation than Pathways, but that is unsurprising since Ray can execute general-purpose Python actors and Pathways is specialized to TPU computations launched from C++. Michael Isard 1 Hyeontaek Lim 1 Ruoming Pang 1 Sudip Roy 1 Brennan Saeta 1 Parker Schuh 1. PATHWAYS is designed to target specic capabilities that we believe will be needed by future ML . Xiao, and Fan Yang. We compare three ways that the user code can enqueue the and scalable pipeline parallel DNN training. Pathways: Asynchronous Distributed Dataflow for ML arxiv.org . The implementation choices made by TF v1 were over-specialized to assume a single, smallish, exclusively-owned island of accelerators. (2015); Mahajan etal. We construct a more realistic pipeline benchmark in which the simple computations from the earlier benchmark are again chained together, but now each computation runs on a different set of 4 TPU cores, each on a different host, and data output from one computation must be sent via ICI before the next computation can execute. Conditionals are functional where both branches have the same output type, and resources are allocated in advance sufficient for either branch. computation graph and hands it off to a coordinator runtime, which (2020) without going via host memory. Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, MLaaS in the wild: Workload analysis and scheduling in large-scale Very happy to see the Pathways paper that I had the great opportunity to work on finally published. (2014); Wentzlaff etal. model parallelism. GPUs use interconnects such as NVLink for high-speed communication between islands of accelerators on a small number of hostsNaumov etal. Guanhua Wang, Kehan Wang, Kenan Jiang, Xiangjun Li, and Ion Stoica. This design, with careful. Pathways: Asynchronous Distributed Dataflow for ML arxiv.org 107 2 Comments Like . 7 , Proceedings of the 5 th MLSys Conference, Santa Clara, CA, USA, 2020; Bai et al., 2020; Yu and Chowdhury, 2020; Wang et al., 2022. We compared JAX and TF models running on their native systems to the same models running on Pathways, and verified that at numerical results are identical, so we focus only on performance. We present the design of a new large scale orchestration layer for accelerators. All other communication across hosts only happens through collectives that use dedicated interconnects like NVLinkFoley and Danskin (2017) and ICIJouppi etal. https://arxiv.org/pdf/2203.12533 ! Modeling task relationships in multi-task learning with multi-gate (2019); Zhang etal. Fine-grained GPU sharing primitives for deep learning applications. 3B Transformer model pipelined over 128 TPUs: Martn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey We want to enable research that uses fine-grain control flow so that different model weights can be updated per example, or even per sub-example (patch of an image, or word of a sentence). at timescales that are significantly smaller than prior work, and for orders-of-magnitude larger pools of resources (e.g., thousands of cores and TBs of accelerator memory). Eric Liang, Melih Elibol, Zongheng Yang, William Paul, MichaelI. Jordan, and This requirement translates to the necessity for Pathways to perform centralized gang-scheduling. Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, and Alex Aiken. This design, with careful engineering, allows Pathways to adopt a single-controller model that makes it easier to express complex new parallelism patterns. 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. When a subgraph of a computation can be scheduled statically, the program sends a single message (describing the entire subgraph) to the scheduler, which is able to sequence the execution of all the active shards in the subgraph back to back. 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. 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). demonstrate that Pathways can achieve performance parity (100 a chained execution of 2 computations A and B with N computation shards each should have 4 nodes in the dataflow representation: ArgCompute(A)Compute(B)Result, regardless of the choice of N. Vineet Gupta, Tomer Koren, and Yoram Singer. Each stage is assigned to a different set of accelerators spanning multiple hosts. AvA: Accelerated virtualization of accelerators. Xiao, and Fan Yang. 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. We also compare against TensorFlow(TF) and Ray in micro-benchmarks, to examine specific aspects of Pathwayss distributed system performance, Transformer models that are pipelined across 16 stages, or sharded across two The biggest difference between TPU and GPU is that far longer-running and more complex computations can be fused into a single TPU kernel, because the TPU supports rich control flow and communication primitives that must instead be executed by driver code on GPU systems. Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Next, we compare the performance of Pathways when training a Transformer-based language model with a Decoder-only architecture on configurations(B) and(C). allainews.com aggregates all of the top news, podcasts and more about AI, Machine Learning, Deep Learning, Computer Vision, NLP and Big Data into one place. Gang scheduling performance benefits for fine-grain synchronization. Pathways: Asynchronous Distributed Dataflow for ML. [2019]. [fontsize=,frame=lines]python Pathways uses a sharded dataflow graph of asynchronous operators . send critical messages with low latency, and batch messages destined for the same host when high throughput is required. of the high level structure that maps sub-computations to accelerators, [2018], TensorFlow [2019] that is able to exploit optimizations like layout assignment and fusion that can substantially improve the efficiency of the resulting accelerator code. "@HPC_Guru @tenstorrent @Microsoft @Google Presentation Video of "Pathways: Asynchronous Distributed Dataflow for ML", Google, Oral, MLSys 2022, Aug 29 https://t.co . Very happy to see the Pathways paper that I had the great opportunity to work on finally published. In such an implementation, Pathways executors and schedulers would be replaced by long-running Ray actors that would implement Pathways scheduling on top of the underlying Ray cluster scheduling, and executors could use PyTorch for GPU computation and collectives. Consider the three-node graph in Figure4, where the squares correspond to three nodes A, B, and C running on accelerators attached to hosts A, B, and C. All node computations are regular compiled functions. Akshay Agrawal, AkshayNaresh Modi, Alexandre Passos, Allen Lavoie, Ashish Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. The key advantage of this architecture is the low latency for dispatching accelerator computations (see Figure1) since an identical copy of the users code runs on each of the accelerator hosts and dispatch involves communication only over (relatively) fast PCIe links. (2020) that is used to express layered DNN models, and we have written a library that automatically converts a FLAX model into a pipelined Pathways program. Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, utilization) with state-of-the-art systems when running SPMD computations over GeoffreyE Hinton, Sara Sabour, and Nicholas Frosst. James Bradbury, Roy Frostig, Peter Hawkins, MatthewJames Johnson, Chris Leary, tensorflow v1 For more complex future multi-tenancy use cases, Pathways will need to handle more diverse resource types including but not limited to device and host memory, and ICI, DCN, and PCIe bandwidth. DCN transfers incur minimal overhead even at the scale of pairs of 128 hosts resulting in 97.2% training throughput compared to an SPMD configuration that uses ICI communication over total equivalent number of chips. The MPI message passing interface standard. 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. library. 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. Contents 0 Operating system interfaces 7 1 Operating system organization 17 2 Page tables 29 3 Traps, interrupts, and drivers 39 4 Locking 51 5 Scheduling 61 6 File system 75 7 Summary 93 A PC hardware 95 B The boot loader 99 Index 105 DRAFT as of September 4, 2018 3 https://pdos.csail.mit.edu/6.828/xv6, Transparency, Distributed, Distributed system, Implementation, System, Service Guidelines for Federal Facilities, Implementation System, Energy, @google.com arXiv:1609.03499v2 [cs.SD] 19, Exploration of neural machine translation, Exploration of neural machine translation architectures, Mastering Chess and Shogi by Self-Play with a . PATHWAYS: ASYNCHRONOUS DISTRIBUTED DATAFLOW FOR ML . 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. The low-level Pathways IR is converted directly to a Plaque program, represented as a dataflow graph. Efficient neural architecture search via parameters sharing. Given the end of Dennard-scaling, accelerators implement hardware parallelism, often using SIMTKirk [2007] or systolic arrayJouppi etal. The multi-controller approach also typically assumes exclusive ownership of hardware resources. Each splitkernel monitor operates locally for its own functionality and only com-. By using our websites, you agree to the placement of these cookies. This experiment highlights that Pathways performs gang-scheduling of programs submitted by 4 independent clients while controlling allocation of accelerator time for fairness; While we do not report detailed results, we have substantial experience of running JAX models on Pathways, which corroborates the finding that the performance of the two systems is comparable across a broad range of settings. Mask: Redesigning the GPU memory hierarchy to support In the remainder of the paper we first discuss the limitations of current distributed ML systems and motivate our design choices for Pathways (2), and next describe the flexible programming model that Pathways supports (3). We present the design of a new large scale orchestration layer for accelerators. then packaged and distributed to other locations. The ability to run unmodified JAX code is convenient but does not unlock the full performance of Pathways. It is the subject of future work to support data-dependent vectorized control flow with both a clean programming model and good performance. For example, hosts for the duration of the program execution. Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie This design, with careful. Shampoo: Preconditioned stochastic tensor optimization. Section3 describes the proposed asynchronous distributed algorithm, with convergence analysis provided in Section4. Zico: Efficient GPU memory sharing for concurrent DNN training. (2011); Vijaykumar etal. (Right) Centralized schedulers for each island gang-schedule computations Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael This non-linear quantization produces a signicantly better reconstruction than a simple linear quantization scheme. We demonstrate that PATHWAYS can achieve performance parity ( 100% accelerator utilization) with state-of-the-art systems when running SPMD computations over 2048 TPUs, while also delivering throughput comparable to the SPMD case for Transformer models that are pipelined across 16 stages, or sharded across two islands of accelerators connected over a data center network. Martin Abadi. (a) Jax or PyTorch SPMD independently enqueues accelerator computations asynchronously over fast PCIe; (b) TensorFlow v1 SPMD requires control messages over slower DCN; (c) TensorFlow v1 non-SPMD programs require cross-host coordination or data transfer through explicit send(S) [2020]. (2019). Accelerator abstractions rely on an asynchronous programming model to achieve performance; a synchronous abstraction wastes too many accelerator computation resources between PCIe latency, kernel scheduling overheads, and interrupt delays. The scheduler must implement policies for allocating accelerators at a time-scale of milliseconds. (2018) and TensorFlow APIs. As expected, for OpByOp the JAX multi-controller throughput is much better than the single-controller systems, particularly as the number of accelerators increases. Deepak Narayanan, Aaron Harlap, Amar Phanishayee, Vivek Seshadri, Nikhil To increase utilization, some ML hardware resource management researchers (Xiao et al. . Since the system can perform resource allocation for compiled functions in advance, contemporary ML frameworks exploit this property by enqueueing compiled functions asynchronously before their predecessors have run, allowing host-side work to be done in parallel with accelerator computationsBradbury etal. Recent work shows that finer-grained sharing can improve resource efficiency further: Andrew G. Howard Menglong Zhu Bo Chen Dmitry Service guidelines for federal facilities, Service Guidelines for Federal Facilities. 9 Examples several researchers might concurrently fine-tune (Houlsby of this architecture include MPI (Clarke et al., 1994), Py- et al., 2019; Zhang et al., 2021) a foundation model for Torch (Paszke et al., 2019), JAX (Bradbury et al., 2018), and different tasks, using the same accelerators to hold the fixed more recent configurations of TensorFlow (Shazeer et al., foundation model layers. These are sub-computations with the following characteristics: Input and output types, and the shapes of any input/output tensors, are known before the input data have been computed. Analysis of large-scale multi-tenant GPU clusters for DNN This design, with careful engineering, allows Pathways to adopt a single-controller model that makes it easier to express complex new parallelism patterns. (2022). The paper has been accepted for publication in MLSys 2022. Given that the compiled functions are all regular, a successor nodes input shapes can in practice be computed before the predecessor computation was even enqueued. Jonathan Heek, Anselm Levskaya, Avital Oliver, Marvin Ritter, Bertrand means that frameworks typically trace the execution of fragments of This routing requires fine-grain data-dependent data exchanges between nodes. Finally, researchers are beginning to standardize on a set of foundation modelsBommasani and et. In Ulf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes When trained using using Pathways over two islands of compute connected over DCN, Pathways achieves 97% of the throughput as compared to a single island with twice as many devices. BEGIN:VCALENDAR VERSION:2.0 PRODID:-//IEEE Santa Clara Valley CIS Chapter - ECPv6.0.2//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:https://r6 . Shubham Chaudhary, Ramachandran Ramjee, Muthian Sivathanu, Nipun Kwatra, and multi-application concurrency. When running computations on accelerators, systems can take advantage of asynchronous APIs to overlap computation with coordinationKwon etal. In addition, JAX supports transforms to vectorize per-example Python functions, producing efficient batched code, and such transforms are a good basis for exploring new forms of data-dependent vectorized control flow, as we briefly describe later (6.3). Zhe Zhao, Lichan Hong, LiWei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee
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