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Efficient memory sharing between CPU and GPU threads can greatly expand the effective set of GPGPU workloads. For increased programmability, this memory should be uniformly virtualized, necessitating compatible address translation support for GPU memory references. However, even a modest GPU might need 100s of translations per cycle (6 CUs * 64 lanes/CU)(More)
Many future heterogeneous systems will integrate CPUs and GPUs physically on a single chip and logically connect them via shared memory to avoid explicit data copying. Making this shared memory coherent facilitates programming and fine-grained sharing, but throughput-oriented GPUs can overwhelm CPUs with coherence requests not well-filtered by caches.(More)
gem5-gpu is a new simulator that models tightly integrated CPU-GPU systems. It builds on gem5, a modular full-system CPU simulator, and GPGPUSim, a detailed GPGPU simulator. gem5-gpu routes most memory accesses through Ruby, which is a highly configurable memory system in gem5. By doing this, it is able to simulate many system configurations, ranging from a(More)
Analytic database workloads are growing in data size and query complexity. At the same time, computer architects are struggling to continue the meteoric increase in performance enabled by Moore's Law. We explore the impact of two emerging architectural trends which may help continue the Moore's Law performance trend for analytic database workloads, namely(More)
As hardware accelerators proliferate, there is a desire to logically integrate them more tightly with CPUs through interfaces such as shared virtual memory. Although this integration has programmability and performance benefits, it may also have serious security and fault isolation implications, especially when accelerators are designed by third parties.(More)
There have been a number of research proposals to use discrete graphics processing units (GPUs) to accelerate database operations. Although many of these works show up to an order of magnitude performance improvement, discrete GPUs are not commonly used in modern database systems. However, there is now a proliferation of integrated GPUs which are on the(More)
Response time requirements for big data processing systems are shrinking. To meet this strict response time requirement, many big data systems store all or most of their data in main memory to reduce the access latency. Main memory capacities have grown, and systems with 2 TB of main memory capacity available today. However, the rate at which processors can(More)
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