Efficient context sensitivity for dynamic analyses via calling context uptrees and customized memory management

Huang, Juan; Bond, Michael D.

doi:10.1145/2544173.2509510

Cited by 5 publications

(3 citation statements)

References 60 publications

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“…Using this nursery size is a good balance between the size of the access trace and the coverage of mature object behaviors. We label allocation sites with unique identifiers, as in Reference [31].…”

Section: Profilingmentioning

confidence: 99%

Reliability-aware Garbage Collection for Hybrid HBM-DRAM Memories

Liu

Akram

Sartor

et al. 2021

ACM Trans. Archit. Code Optim.

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Emerging workloads in cloud and data center infrastructures demand high main memory bandwidth and capacity. Unfortunately, DRAM alone is unable to satisfy contemporary main memory demands. High-bandwidth memory (HBM) uses 3D die-stacking to deliver 4–8× higher bandwidth. HBM has two drawbacks: (1) capacity is low, and (2) soft error rate is high. Hybrid memory combines DRAM and HBM to promise low fault rates, high bandwidth, and high capacity. Prior OS approaches manage HBM by mapping pages to HBM versus DRAM based on hotness (access frequency) and risk (susceptibility to soft errors). Unfortunately, these approaches operate at a coarse-grained page granularity, and frequent page migrations hurt performance. This article proposes a new class of reliability-aware garbage collectors for hybrid HBM-DRAM systems that place hot and low-risk objects in HBM and the rest in DRAM. Our analysis of nine real-world Java workloads shows that: (1) newly allocated objects in the nursery are frequently written, making them both hot and low-risk, (2) a small fraction of the mature objects are hot and low-risk, and (3) allocation site is a good predictor for hotness and risk. We propose RiskRelief, a novel reliability-aware garbage collector that uses allocation site prediction to place hot and low-risk objects in HBM. Allocation sites are profiled offline and RiskRelief uses heuristics to classify allocation sites as DRAM and HBM. The proposed heuristics expose Pareto-optimal trade-offs between soft error rate (SER) and execution time. RiskRelief improves SER by 9× compared to an HBM-Only system while at the same time improving performance by 29% compared to a DRAM-Only system. Compared to a state-of-the-art OS approach for reliability-aware data placement, RiskRelief eliminates all page migration overheads, which substantially improves performance while delivering similar SER. Reliability-aware garbage collection opens up a new opportunity to manage emerging HBM-DRAM memories at fine granularity while requiring no extra hardware support and leaving the programming model unchanged.

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Section: Profilingmentioning

confidence: 99%

Reliability-aware Garbage Collection for Hybrid HBM-DRAM Memories

Liu

Akram

Sartor

et al. 2021

ACM Trans. Archit. Code Optim.

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“…In a recent work , a novel data structure is proposed to avoid the node lookup operation in dynamic bug detectors. A new node is instead allocated for each context, and the costs of allocations are mitigated by extending the garbage collector not only to collect unused node but also to merge duplicate ones lazily.…”

Section: Related Workmentioning

confidence: 99%

Mining hot calling contexts in small space

D’Elia

Demetrescu

Finocchi

2015

Softw. Pract. Exper.

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Summary Calling context trees (CCTs) associate performance metrics with paths through a program's call graph, providing valuable information for program understanding and performance analysis. In real applications, however, CCTs might easily consist of tens of millions of nodes, making them difficult to analyze and also hurting execution times because of poor access locality. For performance analysis, accurately mining only hot calling contexts may be more useful than constructing an entire CCT with millions of uninteresting paths, because the distribution of context frequencies is typically very skewed. In this article, we show how to exploit this property to considerably reduce the CCT size, introducing a novel runtime data structure, called hot CCT (HCCT), in the spectrum of representations for interprocedural control flow. The HCCT includes only hot nodes and their ancestors in a CCT and can be constructed independently from it by using fast, space‐efficient algorithms for mining frequent items in data streams. With this approach, we can distinguish between hot and cold contexts on the fly while obtaining very accurate frequency counts. We show, both theoretically and experimentally, that the HCCT achieves a similar precision as the CCT in a space that is several orders of magnitude smaller and roughly proportional to the number of hot contexts. Our approach can be effectively combined with previous context‐sensitive profiling techniques, as we show for static bursting. We devise an implementation as a plug‐in for the gcc compiler that incurs a slowdown competitive with the gprof call‐graph profiler while collecting finer‐grained profiles. Copyright © 2015 John Wiley & Sons, Ltd.

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“…Sumner et al [23] describe a similar approach for encoding the calling context as a number and report an overhead of 2%. Huang and Bond [13] claim that the accuracy of such approaches does not scale well with program complexity and propose an approach that continuously builds a CCT-like data structure through instrumentation. It creates tree nodes eagerly and relies on a modified garbage collector to release unused nodes and to merge duplicate nodes.…”

Section: Dynamic Analysismentioning

confidence: 99%

Lightweight Java Profiling with Partial Safepoints and Incremental Stack Tracing

Höfer

Gnedt

Mössenböck

2015

Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering

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Sampling profilers are popular because of their low and adjustable overhead and because they do not distort the profile by modifying the application code. A typical sampling profiler periodically suspends the application threads, walks their stacks, and merges the resulting stack traces into a calling context tree. Java virtual machines offer a convenient interface to accomplish this, but rely on safepoints, a synchronization mechanism that requires all threads to park in a safe location. However, a profiler is primarily interested in the running threads, and waiting for all threads to reach a safe location significantly increases the overhead. In most cases, taking a complete stack trace is also unnecessary because many stack frames remain unchanged between samples.We present three techniques that reduce the overhead of sampling Java applications. Partial safepoints require only a certain number of threads to enter a safepoint and can be used to sample only the running threads. With self-sampling, we parallelize taking stack traces by having each thread take its own stack trace. Finally, incremental stack tracing constructs stack traces lazily and examines each stack frame only once instead of walking the entire stack for each sample. Our techniques require no support from the operating system or hardware. With our implementation in the popular HotSpot virtual machine, we show that we can significantly reduce the overhead of sampling without affecting the accuracy of the profiles.

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Efficient context sensitivity for dynamic analyses via calling context uptrees and customized memory management

Cited by 5 publications

References 60 publications

Reliability-aware Garbage Collection for Hybrid HBM-DRAM Memories

Reliability-aware Garbage Collection for Hybrid HBM-DRAM Memories

Mining hot calling contexts in small space

Lightweight Java Profiling with Partial Safepoints and Incremental Stack Tracing

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