Glider: A GPU Library Driver for Improved System Security

Legacy device drivers implement both device resource management and isolation. This results in a large code base with a wide high-level interface making the driver vulnerable to security attacks. This is particularly problematic for increasingly popular accelerators like GPUs that have large, complex drivers. We solve this problem with library drivers, a new driver architecture. A library driver implements resource management as an untrusted library in the application process address space, and implements isolation as a kernel module that is smaller and has a narrower lower-level interface (i.e., closer to hardware) than a legacy driver. We articulate a set of device and platform hardware properties that are required to retrofit a legacy driver into a library driver. To demonstrate the feasibility and superiority of library drivers, we present Glider, a library driver implementation for two GPUs of popular brands, Radeon and Intel. Glider reduces the TCB size and attack surface by about 35% and 84% respectively for a Radeon HD 6450 GPU and by about 38% and 90% respectively for an Intel Ivy Bridge GPU. Moreover, it incurs no performance cost. Indeed, Glider outperforms a legacy driver for applications requiring intensive interactions with the device driver, such as applications using the OpenGL immediate mode API

Distributed Deep Learning for Question Answering

This paper is an empirical study of the distributed deep learning for question answering subtasks: answer selection and question classification. Comparison studies of SGD, MSGD, ADADELTA, ADAGRAD, ADAM/ADAMAX, RMSPROP, DOWNPOUR and EASGD/EAMSGD algorithms have been presented. Experimental results show that the distributed framework based on the message passing interface can accelerate the convergence speed at a sublinear scale. This paper demonstrates the importance of distributed training. For example, with 48 workers, a 24x speedup is achievable for the answer selection task and running time is decreased from 138.2 hours to 5.81 hours, which will increase the productivity significantly.Comment: This paper will appear in the Proceeding of The 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), Indianapolis, US

You and I are Past Our Dancing Days

Operating systems have grown in size and functionality. Today's many flavours of Unix provide a multi-user environment with protection, address spaces, and attempts to allocate resources fairly to users competing for them, They provide processes and threads, mechanisms for synchronization and memory sharing, blocking and nonblocking system calls, and a complex file system. Since it was first introduced, Unix has grown more then a factor twenty in size. Several operating systems now consist of a microkernel, surrounded by user-space services [Accetta et al., 1986; Mullender et al., 1990; Rozier et al., 1988]. Together they provide the functionality of the operating system. This operating system structure provides an opportunity to make operating systems even larger. The trend for operating systems to grow more and more baroque was signalled more than a decade ago [Feldman, 1980], but has continued unabated until, today, we have OSF/1, the most baroque Unix system ever. And we have Windows/NT as a demonstration that MS-DOS also needed to be replaced by something much bigger and a little better.\ud In this position paper, I am asking what community we serve with our operating systems research. Should we continue doing this, or can we make ourselves more useful to society and industry by using our experience in operating systems in new environments.\ud I argue that there is very little need for bigger and better operating systems; that, in fact, most cPus will never run an operating system at all; and that our experience in operating systems will be better applied to designing new generations of distributed and ubiquitous applications