Publications by Mohammad Sadrosadati

Graphics Processing Units (GPUs) employ large register files to accommodate all active threads and accelerate context switching. Unfortunately, register files are a scalability bottleneck for future GPUs due to long access latency, high power consumption, and large silicon area provisioning. Prior work proposes hierarchical register file, to reduce the register file power consumption by caching registers in a smaller register file cache. Unfortunately, this approach does not improve register access latency due to the low hit rate in the register file cache. In this paper, we propose the Latency-Tolerant Register File (LTRF) architecture to achieve low latency in a two-level hierarchical structure while keeping power consumption low. We observe that compile-time interval analysis enables us to divide GPU program execution into intervals with an accurate estimate of a warp’s aggregate register working-set within each interval. The key idea of LTRF is to prefetch the estimated register working-set from the main register file to the register file cache under software control, at the beginning of each interval, and overlap the prefetch latency with the execution of other warps. Our experimental results show that LTRF enables high-capacity yet long-latency main GPU register files, paving the way for various optimizations. As an example optimization, we implement the main register file with emerging high-density high-latency memory technologies, enabling 8× larger capacity and improving overall GPU performance by 31% while reducing register file power consumption by 46%.

@inproceedings{abc,
	abstract = {Graphics Processing Units (GPUs) employ large register files to accommodate all active threads and accelerate context switching. Unfortunately, register files are a scalability bottleneck for future GPUs due to long access latency, high power consumption, and large silicon area provisioning. Prior work proposes hierarchical register file, to reduce the register file power consumption by caching registers in a smaller register file cache. Unfortunately, this approach does not improve register access latency due to the low hit rate in the register file cache. 
In this paper, we propose the Latency-Tolerant Register File (LTRF) architecture to achieve low latency in a two-level hierarchical structure while keeping power consumption low. We observe that compile-time interval analysis enables us to divide GPU program execution into intervals with an accurate estimate of a warp{\textquoteright}s aggregate register working-set within each interval. The key idea of LTRF is to prefetch the estimated register working-set from the main register file to the register file cache under software control, at the beginning of each interval, and overlap the prefetch latency with the execution of other warps. Our experimental results show that LTRF enables high-capacity yet long-latency main GPU register files, paving the way for various optimizations. As an example optimization, we implement the main register file with emerging high-density high-latency memory technologies, enabling 8{\texttimes} larger capacity and improving overall GPU performance by 31\% while reducing register file power consumption by 46\%.},
	author = {Mohammad Sadrosadati and Amirhossein Mirhosseini and Seyed B. Ehsani and Hamid Sarbazi-Azad and Mario Drumond and Babak Falsafi and Rachata Ausavarungnirun and Onur Mutlu},
	booktitle = {Proceedings of the 23rd International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)},
	title = {LTRF: Enabling High-Capacity Register Files for GPUs via Hardware/Software Cooperative Register Prefetching},
	url = {https://dl.acm.org/citation.cfm?id=3173211},
	venue = {Williamsburg, VA, USA},
	year = {2018}
}

Solid-state drives (SSDs) are used in a wide array of computer systems today, including in datacenters and enterprise servers. As the I/O demands of these systems continue to increase, manufacturers are evolving SSD architectures to keep up with this demand. For example, manufacturers have introduced new high-bandwidth interfaces to replace the conventional SATA host-interface protocol. These new interfaces, such as the NVMe protocol, are designed specifically to enable the high amounts of concurrent I/O bandwidth that SSDs are capable of delivering. While modern SSDs with sophisticated features such as the NVMe protocol are already on the market, existing SSD simulation tools have fallen behind, as they do not capture these new features. We find that state-of-the-art SSD simulators have three shortcomings that prevent them from accurately modeling the performance of real off-the-shelf SSDs. First, these simulators do not model critical features of new protocols (e.g., NVMe), such as their use of multiple application-level queues for requests and the elimination of OS intervention for I/O request processing. Second, these simulators often do not accurately capture the impact of advanced SSD maintenance algorithms (e.g., garbage collection), as they do not properly or quickly emulate steady-state conditions that can significantly change the behavior of these algorithms in real SSDs. Third, these simulators do not capture the full end-to-end latency of I/O requests, which can incorrectly skew the results reported for SSDs that make use of emerging non-volatile memory technologies. By not accurately modeling these three features, existing simulators report results that deviate significantly from real SSD performance. In this work, we introduce a new simulator, called MQSim, that accurately models the performance of both modern SSDs and conventional SATA-based SSDs. MQSim faithfully models new high-bandwidth protocol implementations, steady-state SSD conditions, and the full end-to-end latency of requests in modern SSDs. We validate MQSim, showing that it reports performance results that are only 6%-18% apart from the measured actual performance of four real state-of-the-art SSDs. We show that by modeling critical features of modern SSDs, MQSim uncovers several real and important issues that were not captured by existing simulators, such as the performance impact of inter-flow interference. We have released MQSim as an open-source tool, and we hope that it can enable researchers to explore directions in new and different areas.

@inproceedings{abc,
	abstract = {Solid-state drives (SSDs) are used in a wide array of computer systems today, including in datacenters and enterprise servers. As the I/O demands of these systems continue to increase, manufacturers are evolving SSD architectures to keep up with this demand. For example, manufacturers have introduced new high-bandwidth interfaces to replace the conventional SATA host-interface protocol. These new interfaces, such as the NVMe protocol, are designed specifically to enable the high amounts of concurrent I/O bandwidth that SSDs are capable of delivering.

While modern SSDs with sophisticated features such as the NVMe protocol are already on the market, existing SSD simulation tools have fallen behind, as they do not capture these new features. We find that state-of-the-art SSD simulators have three shortcomings that prevent them from accurately modeling the performance of real off-the-shelf SSDs. First, these simulators do not model critical features of new protocols (e.g., NVMe), such as their use of multiple application-level queues for requests and the elimination of OS intervention for I/O request processing. Second, these simulators often do not accurately capture the impact of advanced SSD maintenance algorithms (e.g., garbage collection), as they do not properly or quickly emulate steady-state conditions that can significantly change the behavior of these algorithms in real SSDs. Third, these simulators do not capture the full end-to-end latency of I/O requests, which can incorrectly skew the results reported for SSDs that make use of emerging non-volatile memory technologies. By not accurately modeling these three features, existing simulators report results that deviate significantly from real SSD performance.

In this work, we introduce a new simulator, called MQSim, that accurately models the performance of both modern SSDs and conventional SATA-based SSDs. MQSim faithfully models new high-bandwidth protocol implementations, steady-state SSD conditions, and the full end-to-end latency of requests in modern SSDs. We validate MQSim, showing that it reports performance results that are only 6\%-18\% apart from the measured actual performance of four real state-of-the-art SSDs. We show that by modeling critical features of modern SSDs, MQSim uncovers several real and important issues that were not captured by existing simulators, such as the performance impact of inter-flow interference. We have released MQSim as an open-source tool, and we hope that it can enable researchers to explore directions in new and different areas.},
	author = {Arash Tavakkol and Juan Gomez-Luna and Mohammad Sadrosadati and Saugata Ghose and Onur Mutlu},
	booktitle = {Proceedings of the 16th USENIX Conference on File and Storage Technologies},
	title = {MQsim: a framework for enabling realistic studies of modern multi-queue SSD devices},
	venue = {Oakland, CA, USA},
	year = {2018}
}