In current systems, memory accesses to a DRAM chip must obey a set of minimum latency restrictions specified in the DRAM standard. Such timing parameters exist to guarantee reliable operation. When deciding the timing parameters, DRAM manufacturers incorporate a very large margin as a provision against two worst-case scenarios. First, due to process variation, some outlier chips are much slower than others and cannot be operated as fast. Second, chips become slower at higher temperatures, and all chips need to operate reliably at the highest supported (i.e., worst-case) DRAM temperature (85• C). In this paper, we show that typical DRAM chips operating at typical temperatures (e.g., 55• C) are capable of providing a much smaller access latency, but are nevertheless forced to operate at the largest latency of the worst-case.Our goal in this paper is to exploit the extra margin that is built into the DRAM timing parameters to improve performance. Using an FPGA-based testing platform, we first characterize the extra margin for 115 DRAM modules from three major manufacturers. Our results demonstrate that it is possible to reduce four of the most critical timing parameters by a minimum/maximum of 17.3%/54.8% at 55• C without sacrificing correctness. Based on this characterization, we propose Adaptive-Latency DRAM (AL-DRAM), a mechanism that adaptively reduces the timing parameters for DRAM modules based on the current operating condition. AL-DRAM does not require any changes to the DRAM chip or its interface.We evaluate AL-DRAM on a real system that allows us to reconfigure the timing parameters at runtime. We show that AL-DRAM improves the performance of memory-intensive workloads by an average of 14% without introducing any errors. We discuss and show why AL-DRAM does not compromise reliability. We conclude that dynamically optimizing the DRAM timing parameters can reliably improve system performance.

Modern DRAMs have multiple banks to serve multiple memory requests in parallel. However, when two requests go to the same bank, they have to be served serially, exacerbating the high latency of off-chip memory. Adding more banks to the system to mitigate this problem incurs high system cost. Our goal in this work is to achieve the benefits of increasing the number of banks with a low cost approach. To this end, we propose three new mechanisms that overlap the latencies of different requests that go to the same bank. The key observation exploited by our mechanisms is that a modern DRAM bank is implemented as a collection of subarrays that operate largely independently while sharing few global peripheral structures.Our proposed mechanisms (SALP-1, SALP-2, and MASA) mitigate the negative impact of bank serialization by overlapping different components of the bank access latencies of multiple requests that go to different subarrays within the same bank. SALP-1 requires no changes to the existing DRAM structure and only needs reinterpretation of some DRAM timing parameters. SALP-2 and MASA require only modest changes (< 0.15% area overhead) to the DRAM peripheral structures, which are much less design constrained than the DRAM core. Evaluations show that all our schemes significantly improve performance for both single-core systems and multi-core systems. Our schemes also interact positively with applicationaware memory request scheduling in multi-core systems.

Abstract-Bitwise operations are an important component of modern day programming, and are used in a variety of applications such as databases. In this work, we propose a new and simple mechanism to implement bulk bitwise AND and OR operations in DRAM, which is faster and more efficient than existing mechanisms. Our mechanism exploits existing DRAM operation to perform a bitwise AND/OR of two DRAM rows completely within DRAM. The key idea is to simultaneously connect three cells to a bitline before the sense-amplification. By controlling the value of one of the cells, the sense amplifier forces the bitline to the bitwise AND or bitwise OR of the values of the other two cells. Our approach can improve the throughput of bulk bitwise AND/OR operations by 9.7X and reduce their energy consumption by 50.5X. Since our approach exploits existing DRAM operation as much as possible, it requires negligible changes to DRAM logic. We evaluate our approach using a real-world implementation of a bit-vector based index for databases. Our mechanism improves the performance of commonly-used range queries by 30% on average.

Applications running concurrently on a multicore system in terfere with each other at the main memory. This interference can slow down diff erent applications diff erently. Accurately es timating the slowdown of each application in such a system can enable mechanisms that can enforce quality-of-service. While much prior work has focused on mitigating the performance degradation due to inter-application interference, there is lit tle work on estimating slowdown of individual applications in a multi-programmed environment. Dur goal in th is work is to build such an estimation scheme.To this end, we present our simple Memory-Interference induced Slowdown Estimation (MISE) model that estimates slowdowns caused by memory interference. We build our model based on two observations. First, the performance of a memory bound application is roughly proportional to the rate at which its memory requests are served, suggesting that request-service rate can be used as a proxy for performance. Second, when an application's requests are prioritized over all other applica tions' requests, the application experiences very little interfer ence from other applications. This provides a means for esti mating the uninterfered request-service-rate of an application while it is run alongside other applications. Using the above observations, our model estimates the slowdown of an applica tion as the ra tio of its uninterfered and interfered request service rates. We propose simple changes to the above model to estimate the slowdown of non-memory-bound applications.We demonstrate the effectiveness of our model by develop ing two new memory scheduling schemes: 1) one that provides soft quality-of-service guarantees and 2) another that explicitly attempts to minimize maximum slowdown (i.e., unfairness) in the system. Evaluations show that our techniques perform sig nificantly better than state-of-the-art memory scheduling ap proaches to address the above problems.

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