An official website of the United States government
Temporal prefetchers are powerful because they can prefetch irregular sequences of memory accesses, but temporal prefetchers are commercially infeasible because they store large amounts of metadata in DRAM. This paper presents Triage, the first temporal data prefetcher that does not require off-chip metadata. Triage builds on two insights: (1) Metadata are not equally useful, so the less useful metadata need not be saved, and (2) for irregular workloads, it is more profitable to use portions of the LLC to store metadata than data. We also introduce novel schemes to identify useful metadata, to compress metadata, and to determine the fraction of the LLC to dedicate for metadata.
more »
« less
Temporal Prefetching without the Off-Chip Metadata
Temporal prefetching offers great potential, but this potential is difficult to achieve because of the need to store large amounts of prefetcher metadata off chip. To reduce the latency and traffic of off-chip metadata accesses, recent advances in temporal prefetching have proposed increasingly complex mechanisms that cache and prefetch this off-chip metadata. This paper suggests a return to simplicity: We present a temporal prefetcher whose metadata resides entirely on chip. The key insights are (1) only a small portion of prefetcher metadata is important, and (2) for most workloads with irregular accesses, the benefits of an effective prefetcher outweigh the marginal benefits of a larger data cache. Thus, our solution, the Triage prefetcher, identifies important metadata and uses a portion of the LLC to store this metadata, and it dynamically partitions the LLC between data and metadata. Our empirical results show that when compared against spatial prefetchers that use only on-chip metadata, Triage performs well, achieving speedups on irregular subset of SPEC2006 of 23.5% compared to 5.8% for the previous state-of-the-art. When compared against state-of-the-art temporal prefetchers that use off-chip metadata, Triage sacrifices performance on single-core systems (23.5% speedup vs. 34.7% speedup), but its 62% lower traffic overhead translates to better performance in bandwidth-constrained 16-core systems (6.2% speedup vs. 4.3% speedup).
more »
« less
- Award ID(s):
- 1823546
- PAR ID:
- 10113802
- Date Published:
- Journal Name:
- International Symposium on Microrchitecture
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Temporal prefetchers have the potential to prefetch arbitrary memory access patterns, but they require large amounts of metadata that must typically be stored in DRAM. In 2013, the Irregular Stream Buffer (ISB), showed how this metadata could be cached on chip and managed implicitly by synchronizing its contents with that of the TLB. This paper reveals the inefficiency of that approach and presents a new metadata management scheme that uses a simple metadata prefetcher to feed the metadata cache. The result is the Managed ISB (MISB), a temporal prefetcher that significantly advances the state-of-the-art in terms of both traffic overhead and IPC. Using a highly accurate proprietary simulator for single-core workloads, and using the ChampSim simulator for multi-core workloads, we evaluate MISB on programs from the SPEC CPU 2006 and CloudSuite benchmarks suites. Our results show that for single-core workloads, MISB improves performance by 22.7%, compared to 10.6% for an idealized STMS and 4.5% for a realistic ISB. MISB also significantly reduces off-chip traffic; for SPEC, MISB's traffic overhead of 70% is roughly one fifth of STMS's (342%) and one sixth of ISB's (411%). On 4-core multi-programmed workloads, MISB improves performance by 27.5%, compared to 13.6% for idealized STMS. For CloudSuite, MISB improves performance by 12.8% (vs. 6.0% for idealized STMS), while achieving a traffic reduction of 7 × (83.5% for MISB vs. 572.3% for STMS).more » « less
-
This paper presents Voyager, a novel neural network for data prefetching. Unlike previous neural models for prefetching, which are limited to learning delta correlations, our model can also learn address correlations, which are important for prefetching irregular sequences of memory accesses. The key to our solution is its hierarchical structure that separates addresses into pages and offsets and that introduces a mechanism for learning important relations among pages and offsets. Voyager provides significant prediction benefits over current data prefetchers. For a set of irregular programs from the SPEC 2006 and GAP benchmark suites, Voyager sees an average IPC improvement of 41.6% over a system with no prefetcher, compared with 21.7% and 28.2%, respectively, for idealized Domino and ISB prefetchers. We also find that for two commercial workloads for which current data prefetchers see very little benefit, Voyager dramatically improves both accuracy and coverage. At present, slow training and prediction preclude neural models from being practically used in hardware, but Voyager’s overheads are significantly lower—in every dimension—than those of previous neural models. For example, computation cost is reduced by 15- 20×, and storage overhead is reduced by 110-200×. Thus, Voyager represents a significant step towards a practical neural prefetcher.more » « less
-
The Von Neumann bottleneck is a persistent problem in computer architecture, causing stalls and wasted CPU cycles. The Van Neumann bottleneck is particularly relevant for memory-intensive workloads whose working set does not fit into the microprocessor’s cache and hence memory accesses suffer the high access latency of DRAM. One technique to address this bottleneck is to prefetch data from memory into on-chip caches. While prefetching has proven successful, for simple access patterns such as strides, existing prefetchers are incapable of providing benefit for applications with complex, irregular access patterns. A neural network-based prefetcher shows promise for these challenging workloads. We provide a better understanding of what type of memory access patterns an LSTM neural network can learn by training individual models on microbenchmarks with well-characterized memory access patterns. We explore a range of model parameters and provide a better understanding of what model is ideal to use. We achieve over 95% accuracy on the microbenchmarks and find a strong relationship between lookback (history window) size and the ability of the model to learn the pattern. We find also an upper limit on the number of concurrent distinct memory access streams that can be learned by a model of a given size.more » « less
-
null (Ed.)As multicore systems continue to grow in scale and on-chip memory capacity, the on-chip network bandwidth and latency become problematic bottlenecks. Because of this, overheads in data transfer, the coherence protocol and replacement policies become increasingly important. Unfortunately, even in well-structured programs, many natural optimizations are difficult to implement because of the reactive and centralized nature of traditional cache hierarchies, where all requests are initiated by the core for short, cache line granularity accesses. For example, long-lasting access patterns could be streamed from shared caches without requests from the core. Indirect memory access can be performed by chaining requests made from within the cache, rather than constantly returning to the core. Our primary insight is that if programs can embed information about long-term memory stream behavior in their ISAs, then these streams can be floated to the appropriate level of the memory hierarchy. This decentralized approach to address generation and cache requests can lead to better cache policies and lower request and data traffic by proactively sending data before the cores even request it. To evaluate the opportunities of stream floating, we enhance a tiled multicore cache hierarchy with stream engines to process stream requests in last-level cache banks. We develop several novel optimizations that are facilitated by stream exposure in the ISA, and subsequent exposure to caches. We evaluate using a cycle-level execution-driven gem5-based simulator, using 10 data-processing workloads from Rodinia and 2 streaming kernels written in OpenMP. We find that stream floating enables 52% and 39% speedup over an inorder and OOO core with state of art prefetcher design respectively, with 64% and 49% energy efficiency advantage.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
