Proceedings of the 18th International Symposium on Code Generation and Optimization
CGO, 2020.

@proceedings{CGO-2020,
	doi           = "10.1145/3368826",
	isbn          = "978-1-4503-7047-9",
	publisher     = "{ACM}",
	title         = "{Proceedings of the 18th International Symposium on Code Generation and Optimization}",
	year          = 2020,
}

Contents (25 items)

CGO-2020-IsmailS #manycore #performance

Efficient nursery sizing for managed languages on multi-core processors with shared caches (MI, GES), pp. 1–15.

CGO-2020-ChengIBB #compilation #jit #morphism

Type freezing: exploiting attribute type monomorphism in tracing JIT compilers (LC, BI, CFBT, CB), pp. 16–29.

CGO-2020-ParkLZM #fault #low cost #predict

Low-cost prediction-based fault protection strategy (SP, SL, ZZ, SAM), pp. 30–42.

CGO-2020-OjogboTV #automation #bound

Secure automatic bounds checking: prevention is simpler than cure (EJO, MT, TNV), pp. 43–55.

CGO-2020-JoshiFM #approximate #named #reliability #source code #verification

Aloe: verifying reliability of approximate programs in the presence of recovery mechanisms (KJ, VF, SM), pp. 56–67.

CGO-2020-VermaKPR #concurrent #debugging #interactive #memory management #modelling #source code

Interactive debugging of concurrent programs under relaxed memory models (AV, PKK, AP, SR), pp. 68–80.

CGO-2020-TanejaLR #analysis #precise #testing

Testing static analyses for precision and soundness (JT, ZL, JR), pp. 81–93.

CGO-2020-SavageJ #named #optimisation

HALO: post-link heap-layout optimisation (JS, TMJ0), pp. 94–106.

CGO-2020-WangYZM #hardware #memory management #performance #scalability #transaction

Efficient and scalable cross-ISA virtualization of hardware transactional memory (WW, PCY, AZ, SM), pp. 107–120.

CGO-2020-DamaniJSKYMG #performance

Speculative reconvergence for improved SIMT efficiency (SD, DRJ, MS, SWK, EY, MM, OG), pp. 121–132.

CGO-2020-ShobakiKM #approach #combinator #gpu #optimisation #using

Optimizing occupancy and ILP on the GPU using a combinatorial approach (GS, AK, SM), pp. 133–144.

CGO-2020-ShaikhhaSGO #data analysis #multi #optimisation

Multi-layer optimizations for end-to-end data analytics (AS, MS, AG, DO), pp. 145–157.

CGO-2020-ZhangBCDKAS #algorithm #graph #optimisation #order

Optimizing ordered graph algorithms with GraphIt (YZ, AB, XC, LD, SK, SPA, JS), pp. 158–170.

CGO-2020-LovelessOB #compilation #cyber-physical

A performance-optimizing compiler for cyber-physical digital microfluidic biochips (TL, JO, PB), pp. 171–184.

CGO-2020-KrugerAB #api #generative #named

CogniCryptGEN: generating code for the secure usage of crypto APIs (SK, KA0, EB), pp. 185–198.

CGO-2020-MatsumuraZWEM #automation #framework #named

AN5D: automated stencil framework for high-degree temporal blocking on GPUs (KM, HRZ, MW, TE, SM), pp. 199–211.

CGO-2020-DakkakWH #compilation #design #implementation

The design and implementation of the wolfram language compiler (AD, TWJ, WMH), pp. 212–228.

CGO-2020-EidtG

SIMD support in .NET: abstract and concrete vector types and operations (CE, TG), pp. 229–241.

CGO-2020-Haj-AliAWSAS #learning #named

NeuroVectorizer: end-to-end vectorization with deep reinforcement learning (AHA, NKA, TLW, YSS, KA, IS), pp. 242–255.

CGO-2020-LeonardC #compilation #generative #pseudo

Introducing the pseudorandom value generator selection in the compilation toolchain (ML, SC), pp. 256–267.

CGO-2020-YuPJLT #collaboration #manycore #named #symmetry

COLAB: a collaborative multi-factor scheduler for asymmetric multicore processors (TY, PP, VJ, HL, JT), pp. 268–279.

CGO-2020-KangCP #framework #named #performance #precise #scalability

PreScaler: an efficient system-aware precision scaling framework on heterogeneous systems (SK, KC, YP), pp. 280–292.

CGO-2020-ChenPPLR #adaptation #graph #named

ATMem: adaptive data placement in graph applications on heterogeneous memories (YC, IBP, ZP, XL, BR), pp. 293–304.

CGO-2020-CowanMCBC #automation #generative #kernel #machine learning

Automatic generation of high-performance quantized machine learning kernels (MC, TM, TC, JB, LC), pp. 305–316.

CGO-2020-JavanmardAKPCH #algorithm #compilation #divide and conquer #multi #parametricity #programming #recursion #using

Deriving parametric multi-way recursive divide-and-conquer dynamic programming algorithms using polyhedral compilers (MMJ, ZA, MK, LNP, RC, RJH), pp. 317–329.