Related papers: RDMA is Turing complete, we just did not know it y…
High-performance clusters and datacenters pose increasingly demanding requirements on storage systems. If these systems do not operate at scale, applications are doomed to become I/O bound and waste compute cycles. To accelerate the data…
In this work, we aim to evaluate different Distributed Lock Management service designs with Remote Direct Memory Access (RDMA). In specific, we implement and evaluate the centralized and the RDMA-enabled lock manager designs for fast…
Today's data centers consist of thousands of network-connected hosts, each with CPUs and accelerators such as GPUs and FPGAs. These hosts also contain network interface cards (NICs), operating at speeds of 100Gb/s or higher, that are used…
Remote Direct Memory Access (RDMA) is a technology that allows direct memory access from the memory of one computer into that of another without involving either one's operating system. This enables high-throughput, low-latency networking,…
In order to deliver high performance in cloud computing, we generally exploit and leverage RDMA (Remote Direct Memory Access) in networking and NVM (Non-Volatile Memory) in end systems. Due to no involvement of CPU, one-sided RDMA becomes…
From hardware offloads like RDMA to software ones like eBPF, offloads are everywhere and their value is in performance. However, there is evidence that fully offloading -- even when feasible -- does not always give the expected speedups.…
Combining persistent memory (PM) with RDMA is a promising approach to performant replicated distributed key-value stores (KVSs). However, existing replication approaches do not work well when applied to PM KVSs: 1) Using RPC induces…
Remote Direct Memory Access (RDMA) is a key enabler of high-performance systems, offering low latency, high throughput, and reduced CPU overhead by allowing direct memory-to-memory transfers between machines. However, its design bypasses…
Coordinating concurrent access to a shared resource using mutual exclusion is a fundamental problem in computation. In this paper, we present a novel approach to mutual exclusion designed specifically for distributed systems leveraging a…
Nowadays, avoiding system calls during cluster communication (e.g., in Data Centers and High Performance Computing) in modern high-speed interconnection networks has become a necessity, due to the high overhead of multiple data copies…
Remote Direct Memory Access (RDMA) has been haunted by the need of pinning down memory regions. Pinning limits the memory utilization because it impedes on-demand paging and swapping. It also increases the initialization latency of large…
Remote direct memory access (RDMA) allows a machine to directly read from and write to the memory of remote machine, enabling high-throughput, low-latency data transfer. Ensuring correctness of RDMA programs has only recently become…
Remote Direct Memory Access (RDMA) improves host networking performance by eliminating software and server CPU involvement. However, RDMA has a limited set of operations, is difficult to program, and often requires multiple round trips to…
Remote Direct Memory Access (RDMA) is an efficient way to improve the performance of traditional client-server systems. Currently, there are two main design paradigms for RDMA-accelerated systems. The first allows the clients to directly…
Distributed data structures are key to implementing scalable applications for scientific simulations and data analysis. In this paper we look at two implementation styles for distributed data structures: remote direct memory access (RDMA)…
Remote direct memory access (RDMA) networks are being rapidly adopted into industry for their high speed, low latency, and reduced CPU overheads compared to traditional kernel-based TCP/IP networks. RDMA enables threads to access remote…
We present RDMAbox, a set of low level RDMA optimizations that provide better performance than previous approaches. The optimizations are packaged in easy-to-use kernel and user space libraries for applications and systems in data center.…
In this paper, we conduct systematic measurement studies to show that the high memory bandwidth consumption of modern distributed applications can lead to a significant drop of network throughput and a large increase of tail latency in…
Deep learning emerges as an important new resource-intensive workload and has been successfully applied in computer vision, speech, natural language processing, and so on. Distributed deep learning is becoming a necessity to cope with…
Popular deep learning frameworks require users to fine-tune their memory usage so that the training data of a deep neural network (DNN) fits within the GPU physical memory. Prior work tries to address this restriction by virtualizing the…