December 19th, Wednesday 14:15, Room 303, Jacobs Building

Title: Operating system support for high-throughput processors

Lecturer: Mark Silberstein

Lecturer homepage : https://sites.google.com/site/silbersteinmark/

Affiliation : Department of Computer Science, UT Austin

 

The processor landscape has fractured into latency-optimized CPUs, throughput-oriented GPUs, and soon, custom accelerators. Future applications will need to cohesively use a variety of hardware to achieve their performance and power goals. However building efficient systems that use accelerators today is incredibly difficult.

In this talk I will argue that the root cause of this complexity lies in the lack of adequate operating system support for accelerators. While operating systems provide optimized resource management and Input/Output (I/O) services to CPU applications, they make no such services available to accelerator programs.

I propose GPUfs - an operating system layer which enables access to files directly from programs running on throughput-oriented accelerators, such as GPUs. GPUfs extends the constrained GPU-as-coprocessor programming model, turning GPUs into first-class computing devices with full file I/O support. It provides a POSIX-like API for GPU programs, exploits parallelism for efficiency, and optimizes for access locality by extending a CPU buffer cache into physical memories of all GPUs in a single machine.

Using real benchmarks I show that GPUfs simplifies the development of efficient applications by eliminating the GPU management complexity, and broadens the range of applications that can be accelerated by GPUs. For example, a simple self-contained GPU program which searches for a set of strings in the entire tree of Linux kernel source files completes in about third of the time of an 8-CPU-core run.

Joint work with Idit Keidar (Technion), Bryan Ford (Yale) and Emmett Witchel (UT Austin)

Bio:

Mark Silberstein is a postdoctoral fellow in the Operating Systems Architecture group at the University of Texas at Austin. He holds a PhD in Computer Science from the Technion. His thesis focused on parallel algorithms and resource management in high-performance large-scale distributed systems. His research in GPU computing includes acceleration of memory-intensive applications (ICS08), power-efficient resource allocation in CPU-GPU hybrids (SYSTOR11), hard real-time stream processing on GPUs (ICS11), operating system abstractions for GPUs (SOSP11), operating system support for privacy (OSDI12), and I/O services for GPUs (ASPLOS13).