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Abstract—Dense linear algebra kernels are critical for wireless, and the oncoming proliferation of 5G only amplifies their importance. Due to the inductive nature of many such algorithms, parallelism is difficult to exploit: parallel regions have fine grain producer/consumer interaction with iteratively changing dependence distance, reuse rate, and memory access patterns. This causes a high overhead both for multi-threading due to fine-grain synchronization, and for vectorization due to the nonrectangular iteration domains. CPUs, DSPs, and GPUs perform order-of-magnitude below peak. Our insight is that if the nature of inductive dependences and memory accesses were explicit in the hardware/software interface, then a spatial architecture could efficiently execute parallel code regions. To this end, we first extend the traditional dataflow model with first class primitives for inductive dependences and memory access patterns (streams). Second, we develop a hybrid spatial architecture combining systolic and dataflow execution to attain high utilization at low energy and area cost. Finally, we create a scalable design through a novel vector-stream control model which amortizes control overhead both in time and spatially across architecture lanes. We evaluate our design, REVEL, with a full stack (compiler, ISA, simulator, RTL). Across a suite of linear algebra kernels, REVEL outperforms equally-provisioned DSPs by 4.6×—37×. Compared to state-of-the-art spatial architectures, REVEL is mean 3.4× faster. Compared to a set of ASICs, REVEL is only 2× the power and half the area.
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Stream-based memory access specialization for general purpose processors
Because of severe limitations in technology scaling, architects have innovated in specializing general purpose processors for computation primitives (e.g. vector instructions, loop accelerators). The general principle is exposing rich semantics to the ISA. An opportunity to explore is whether richer semantics of memory access patterns could also be used to improve the efficiency of memory and communication. Two important open questions are how to convey higher level memory information and how to take advantage of this information in hardware. We find that a majority of memory accesses follow a small number of simple patterns; we term these streams (e.g. affine, indirect). Streams can often be decoupled from core execution, and patterns persist long enough to express useful behavior. Our approach is therefore to express streams as ISA primitives, which we argue can enable: prefetch stream accesses to hide memory latency, semi-binding decoupled access to remove address computation and optimize the memory interface, and finally inform cache policies. In this work, we propose ISA-extensions for decoupled-streams, which interact with the core using a FIFO-based interface. We implement optimizations for each of the aforementioned opportunities on an aggressive wide-issue OOO core and evaluate with SPEC CPU 2017 and CortexSuite[1, 2]. Across all workloads, we observe about 1.37× speedup and energy efficiency improvement over hardware stride prefetching.
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- Award ID(s):
- 1823562
- PAR ID:
- 10120212
- Date Published:
- Journal Name:
- ISCA
- Page Range / eLocation ID:
- 736 to 749
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3307650.3322229
