An official website of the United States government
The growing adoption of hardware accelerators driven by their intelligent compiler and runtime system counterparts has democratized ML services and precipitously reduced their execution times. This motivates us to shift our attention to efficiently serve these ML services under distributed settings and characterize the overheads imposed by the RPC mechanism ('RPC tax') when serving them on accelerators. The RPC implementations designed over the years implicitly assume the host CPU services the requests, and we focus on expanding such works towards accelerator-based services. While recent proposals calling for SmartNICs to take on this task are reasonable for simple kernels, serving complex ML models requires a more nuanced view to optimize both the data-path and the control/orchestration of these accelerators. We program today's commodity network interface cards (NICs) to split the control and data paths for effective transfer of control while efficiently transferring the payload to the accelerator. As opposed to unified approaches that bundle these paths together, limiting the flexibility in each of these paths, we design and implement SplitRPC - a control + data path optimizing RPC mechanism for ML inference serving. SplitRPC allows us to optimize the datapath to the accelerator while simultaneously allowing the CPU to maintain full orchestration capabilities. We implement SplitRPC on both commodity NICs and SmartNICs and demonstrate how GPU-based ML services running different compiler/runtime systems can benefit. For a variety of ML models served using different inference runtimes, we demonstrate that SplitRPC is effective in minimizing the RPC tax while providing significant gains in throughput and latency over existing kernel by-pass approaches, without requiring expensive SmartNIC devices.
more »
« less
Ship Compute or Ship Data? Why Not Both?
How cloud applications should interact with their data remains an active area of research. Over the last decade, many have suggested relying on a key-value (KV) interface to interact with data stored in remote storage servers, while others have vouched for the benefits of using remote procedure call (RPC). Instead of choosing one over the other, in this paper, we observe that an ideal solution must adaptively combine both of them in order to maximize throughput while meeting application latency requirements. To this end, we propose a new system called Kayak that proactively adjusts the rate of requests and the fraction of requests to be executed using RPC or KV, all in a fully decentralized and self-regulated manner. We theoretically prove that Kayak can quickly converge to the optimal parameters. We implement a system prototype of Kayak. Our evaluations show that Kayak achieves sub-second convergence and improves overall throughput by 32.5%-63.4% for compute-intensive workloads and up to 12.2% for non-compute-intensive and transactional workloads over the state-of-the-art.
more »
« less
- Award ID(s):
- 1918757
- PAR ID:
- 10283422
- Date Published:
- Journal Name:
- 18th USENIX Symposium on Networked Systems Design and Implementation
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The growing adoption of hardware accelerators driven by their intelligent compiler and runtime system counterparts has democratized ML services and precipitously reduced their execution times. This motivates us to shift our attention to efficiently serve these ML services under distributed settings and characterize the overheads imposed by the RPC mechanism (‘RPC tax’) when serving them on accelerators. The RPC implementations designed over the years implicitly assume the host CPU services the requests, and we focus on expanding such works towards accelerator-based services. While recent proposals calling for SmartNICs to take on this task are reasonable for simple kernels, serving complex ML models requires a more nuanced view to optimize both the data-path and the control/orchestration of these accelerators. We program today’s commodity network interface cards (NICs) to split the control and data paths for effective transfer of control while efficiently transferring the payload to the accelerator. As opposed to unified approaches that bundle these paths together, limiting the flexibility in each of these paths, we design and implement SplitRPC - a {control + data} path optimizing RPC mechanism for ML inference serving. SplitRPC allows us to optimize the datapath to the accelerator while simultaneously allowing the CPU to maintain full orchestration capabilities. We implement SplitRPC on both commodity NICs and SmartNICs and demonstrate how GPU-based ML services running different compiler/runtime systems can benefit. For a variety of ML models served using different inference runtimes, we demonstrate that SplitRPC is effective in minimizing the RPC tax while providing significant gains in throughput and latency over existing kernel by-pass approaches, without requiring expensive SmartNIC devices.more » « less
-
While permissioned blockchains enable a family of data center applications, existing systems suffer from imbalanced loads across compute and memory, exacerbating the underutilization of cloud resources. This paper presents FlexChain , a novel permissioned blockchain system that addresses this challenge by physically disaggregating CPUs, DRAM, and storage devices to process different blockchain workloads efficiently. Disaggregation allows blockchain service providers to upgrade and expand hardware resources independently to support a wide range of smart contracts with diverse CPU and memory demands. Moreover, it ensures efficient resource utilization and hence prevents resource fragmentation in a data center. We have explored the design of XOV blockchain systems in a disaggregated fashion and developed a tiered key-value store that can elastically scale its memory and storage. Our design significantly speeds up the execution stage. We have also leveraged several techniques to parallelize the validation stage in FlexChain to further improve the overall blockchain performance. Our evaluation results show that FlexChain can provide independent compute and memory scalability, while incurring at most 12.8% disaggregation overhead. FlexChain achieves almost identical throughput as the state-of-the-art distributed approaches with significantly lower memory and CPU consumption for compute-intensive and memory-intensive workloads respectively.more » « less
-
It is becoming increasingly popular for distributed systems to exploit offload to reduce load on the CPU. Remote Direct Memory Access (RDMA) offload, in particular, has become popular. However, RDMA still requires CPU intervention for complex offloads that go beyond simple remote memory access. As such, the offload potential is limited and RDMA-based systems usually have to work around such limitations. We present RedN, a principled, practical approach to implementing complex RDMA offloads, without requiring any hardware modifications. Using self-modifying RDMA chains, we lift the existing RDMA verbs interface to a Turing complete set of programming abstractions. We explore what is possible in terms of offload complexity and performance with a commodity RDMA NIC. We show how to integrate these RDMA chains into applications, such as the Memcached key-value store, allowing us to offload complex tasks such as key lookups. RedN can reduce the latency of key-value get operations by up to 2.6× compared to state-of-the-art KV designs that use one-sided RDMA primitives (e.g., FaRM-KV), as well as traditional RPC-over-RDMA approaches. Moreover, compared to these baselines, RedN provides performance isolation and, in the presence of contention, can reduce latency by up to 35× while providing applications with failure resiliency to OS and process crashes.more » « less
-
R-tree is a foundational data structure used in spatial databases and scientific databases. With the advancement of networks and computer architectures, in-memory data processing for R-tree in distributed systems has become a common platform. We have observed new performance challenges to process R-tree as the amount of multidimensional datasets become increasingly high. Specifically, an R-tree server can be heavily overloaded while the network and client CPU are lightly loaded, and vice versa. In this article, we present the design and implementation of Catfish, an RDMA-enabled R-tree for low latency and high throughput by adaptively utilizing the available network bandwidth and computing resources to balance the workloads between clients and servers. We design and implement two basic mechanisms of using RDMA for a client-server R-tree data processing system. First, in the fast messaging design, we use RDMA writes to send R-tree requests to the server and let server threads process R-tree requests to achieve low query latency. Second, in the RDMA offloading design, we use RDMA reads to offload tree traversal from the server to the client, which rescues the server as it is overloaded. We further develop an adaptive scheme to effectively switch an R-tree search between fast messaging and RDMA offloading, maximizing the overall performance. Our experiments show that the adaptive solution of Catfish on InfiniBand significantly outperforms R-tree that uses only fast messaging or only RDMA offloading in both latency and throughput. Catfish can also deliver up to one order of magnitude performance over the traditional schemes using TCP/IP on 1 and 40 Gbps Ethernet. We make a strong case to use RDMA to effectively balance workloads in distributed systems for low latency and high throughput.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
