PCIe Device Lending

We have developed a proof of concept for allowing a PCI Express device attached to one computer to be used by another computer without any software intermediate on the data path. The device driver runs on a physically separate machine from the device, but our implementation allows the device driver and device to communicate as if the device and driver were in the same machine, without modifying either the driver or the device. The kernel and higher level software can utilize the device as if it were a local device. A device will not be used by two separate machines at the same time, but a machine can transfer the control of a local device to a remote machine. We have named this concept "device lending". We envision that machines will have, in addition to local PCIe devices, access to a pool of remote PCIe devices. When a machine needs more device resources, additional devices can be dynamically borrowed from other machines with devices to spare. These devices can be located in a dedicated external cabinet, or be devices inserted into internal slots in a normal computer. The device lending is implemented using a Non-Transparent Bridge (NTB), a native PCIe interconnect that should offer performance close to that of a locally connected device. Devices that are not currently being lent to another host will not be affected in any way. NTBs are available as add-ons for any PCIe based computer and are included in newer Intel Xeon CPUs. The proof of concept we created was implemented for Linux, on top of the APIs provided by our NTB vendor, Dolphin. The host borrowing a device has a kernel module to provide the necessary software support and the other host has a user space daemon. No additional software modifications or hardware is required, nor special support from the devices. The current implementation works with some devices, but has some problems with others. We believe however, that we have identified the problems and how to improve the situation. In a later implementation, we believe that all devices we have tested can be made to work correctly and with very high performance

SmartIO: Zero-overhead Device Sharing through PCIe Networking

The large variety of compute-heavy and data-driven applications accelerate the need for a distributed I/O solution that enables cost-effective scaling of resources between networked hosts. For example, in a cluster system, different machines may have various devices available at different times, but moving workloads to remote units over the network is often costly and introduces large overheads compared to accessing local resources. To facilitate I/O disaggregation and device sharing among hosts connected using Peripheral Component Interconnect Express (PCIe) non-transparent bridges, we present SmartIO. NVMes, GPUs, network adapters, or any other standard PCIe device may be borrowed and accessed directly, as if they were local to the remote machines. We provide capabilities beyond existing disaggregation solutions by combining traditional I/O with distributed shared-memory functionality, allowing devices to become part of the same global address space as cluster applications. Software is entirely removed from the data path, and simultaneous sharing of a device among application processes running on remote hosts is enabled. Our experimental results show that I/O devices can be shared with remote hosts, achieving native PCIe performance. Thus, compared to existing device distribution mechanisms, SmartIO provides more efficient, low-cost resource sharing, increasing the overall system performance

SmartIO: Zero-overhead Device Sharing through PCIe Networking

The large variety of compute-heavy and data-driven applications accelerate the need for a distributed I/O solution that enables cost-effective scaling of resources between networked hosts. For example, in a cluster system, different machines may have various devices available at different times, but moving workloads to remote units over the network is often costly and introduces large overheads compared to accessing local resources. To facilitate I/O disaggregation and device sharing among hosts connected using Peripheral Component Interconnect Express (PCIe) non-transparent bridges, we present SmartIO. NVMes, GPUs, network adapters, or any other standard PCIe device may be borrowed and accessed directly, as if they were local to the remote machines. We provide capabilities beyond existing disaggregation solutions by combining traditional I/O with distributed shared-memory functionality, allowing devices to become part of the same global address space as cluster applications. Software is entirely removed from the data path, and simultaneous sharing of a device among application processes running on remote hosts is enabled. Our experimental results show that I/O devices can be shared with remote hosts, achieving native PCIe performance. Thus, compared to existing device distribution mechanisms, SmartIO provides more efficient, low-cost resource sharing, increasing the overall system performance

Device Lending in PCI Express Networks

ABSTRACT The challenge of scaling IO performance of multimedia systems to demands of their users has attracted much research. A lot of effort has gone into development of distributed systems that add little latency and computing overhead. For machines in PCI Express (PCIe) clusters, we propose Device Lending as a novel solution which works at a system level. Device Lending achieves low latency and extremely low computing overhead without requiring any application-specific distribution mechanisms. For applications, the remote IO resource appears local. In fact, even the drivers of the operating system remain unaware that hardware resources are located in remote machines. By enabling machines in a PCIe cluster to lend a wide variety of hardware, cluster machines can get temporary access to a pool of IO resources. Network cards, FPGAs, SSDs, and even GPUs can easily be shared among computers. Our proposed solution, Device Lending, works transparently without requiring any modifications to drivers, operating systems or software applications

Flexible device compositions and dynamic resource sharing in PCIe interconnected clusters using Device Lending

Modern workloads often exceed the processing and I/O capabilities provided by resource virtualization, requiring direct access to the physical hardware in order to reduce latency and computing overhead. For computers interconnected in a cluser, access to remote hardware resources often requires facilitation both in hardware and specialized drivers with virtualization support. This limits the availability of resources to specific devices and drivers that are supported by the virtualization technology being used, as well as what the interconnection technology supports. For PCI Express (PCIe) clusters, we have previously proposed Device Lending as a solution for enabling direct low latency access to remote devices. The method has extremely low computing overhead, and does not require any application- or device-specific distribution mechanisms. Any PCIe device, such as network cards disks, and GPUs, can easily be shared among the connected hosts. In this work, we have extended our solution with support for a virtual machine (VM) hypervisor. Physical remote devices can be “passed through” to VM guests, enabling direct access to physical resources while still retaining the flexibility of virtualization. Additionally, we have also implemented multi-device support, enabling shortest-path peer-to-peer transfers between remote devices residing in different hosts.Our experimental results prove that multiple remote devices can be used, achieving bandwidth and latency close to native PCIe, and without requiring any additional support in device drivers. I/O intensive workloads run seamlessly using both local and remote resources. With our added VM and multi-device support, Device Lending offers highly customizable configurations of remote devices that can be dynamically reassigned and shared to optimize resource utilization, thus enabling a flexible composable I/O infrastructure for VMs as well as bare-metal machines