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1. Scalability of Streaming on Migrating Threads

   Page, Brian A.; Kogge, Peter M. (September 2020, IEEE High Performance Extreme Computing Conference)

   null (Ed.)

   Applications where streams of data are passed through large data structures are becoming of increasing importance.Unfortunately, when implemented on conventional architectures such applications become horribly inefficient, especially when attempts are made to scale up performance via some sort of parallelism. This paper discusses the implementation of the Firehose streaming benchmark on a novel parallel architecture with greatly enhanced multi-threading characteristics that avoids the conventional inefficiencies. Results are promising, with both far better scaling and increased performance over previously reported implementations, on a prototype platform with considerably less intrinsic hardware computational resources. 

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2. Scalability of streaming on migrating threads

   Page, Brian A.; Kogge, Peter M. (September 2020, IEEE High Performance Extreme Computing Conf. (HPEC))

   null (Ed.)

   Applications where streams of data are passed through large data structures are becoming of increasing importance.Unfortunately, when implemented on conventional architectures such applications become horribly inefficient, especially when attempts are made to scale up performance via some sort of parallelism. This paper discusses the implementation of the Firehose streaming benchmark on a novel parallel architecture with greatly enhanced multi-threading characteristics that avoids the conventional inefficiencies. Results are promising, with both far better scaling and increased performance over previously reported implementations, on a prototype platform with considerably less intrinsic hardware computational resources. 

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3. Physics-inspired heuristics for soft MIMO detection in 5G new radio and beyond

   https://doi.org/10.1145/3447993.3448619  

   Kim, Minsung; Mandrà, Salvatore; Venturelli, Davide; Jamieson, Kyle (January 2021, Proceedings of the 27th Annual International Conference on Mobile Computing and Networking)

   null (Ed.)

   Overcoming the conventional trade-off between throughput and bit error rate (BER) performance, versus computational complexity is a long-term challenge for uplink Multiple-Input Multiple-Output (MIMO) detection in base station design for the cellular 5G New Radio roadmap, as well as in next generation wireless local area networks. In this work, we present ParaMax, a MIMO detector architecture that for the first time brings to bear physics-inspired parallel tempering algorithmic techniques [28, 50, 67] on this class of problems. ParaMax can achieve near optimal maximum-likelihood (ML) throughput performance in the Large MIMO regime, Massive MIMO systems where the base station has additional RF chains, to approach the number of base station antennas, in order to support even more parallel spatial streams. ParaMax is able to achieve a near ML-BER performance up to 160 × 160 and 80 × 80 Large MIMO for low-order modulations such as BPSK and QPSK, respectively, only requiring less than tens of processing elements. With respect to Massive MIMO systems, in 12 × 24 MIMO with 16-QAM at SNR 16 dB, ParaMax achieves 330 Mbits/s near-optimal system throughput with 4--8 processing elements per subcarrier, which is approximately 1.4× throughput than linear detector-based Massive MIMO systems. 

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4. Locality: The 3rd Wall and The Need for Innovation in Parallel Architectures

   Kogge, Peter M; Page, Brian A (June 2021, 34th GI/ITG International Conference on Architecture of Computing Systems)

   null (Ed.)

   In the past we have seen two major \walls" (memory and power) whose vanquishing required significant advances in architecture. This paper discusses evidence of a third wall dealing with data locality, which is prevalent in data intensive applications where computation is dominated by memory access and movement - not flops, Such apps exhibit large sets of often persistent data, with little reuse during computation, no predictable regularity, significantly different scaling characteristics, and where streaming is becoming important. Further, as we move to highly parallel algorithms (as in running in the cloud), these issues will get even worse. Solving such problems will take a new set of innovations in architecture. In addition to data on the new wall, this paper will look at one possible technique: the concept of migrating threads, and give evidence of its potential value based on several benchmarks that have scaling difficulties on conventional architectures. 

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5. Deluge: Achieving Superior Efficiency, Throughput, and Scalability with Actor Based Streaming on Migrating Threads

   Page, Brian A; Kogge, Peter M (September 2021, IEEE High Performance Extreme Computing Conference)

   null (Ed.)

   Applications where streams of data are passed through large data structures are becoming of increasing importance. For instance network intrusion detection and cyber security as a whole rely on real time analysis of network traffic. Unfortunately, when implemented on conventional architectures such applications become horribly inefficient, especially when attempts are made to scale up performance via some sort of parallelism. An earlier paper discussed streaming anomaly detection within a stream having an unbounded range of keys on the Lucata migrating thread architecture. In this paper we introduce \textit{Deluge}, a new implementation that addresses several inadequacies of previous designs and seeks to more directly target the hardware efficiencies inherent to migratory execution within a PGAS address space. Deluge achieves major improvements in hardware efficiency, throughput, and scalability over previous implementations. 

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