Towards a Linear-Algebraic Hypervisor
career value
256K/year
🤖 AI Summary
General-purpose programs often struggle to achieve efficient parallel execution on GPUs, hindering advances in program synthesis, superoptimization, and array programming. To address this limitation, this work proposes a highly parallel virtual machine architecture tailored for linear algebra computations on GPUs, enabling efficient execution of massively concurrent array programs. By integrating a parallel virtual machine, an array-program execution model, and a scalable evaluation framework, the proposed architecture achieves up to a 147× speedup over serial execution across workloads involving millions of concurrent tasks. This substantial acceleration significantly enhances GPU resource utilization and improves the efficiency of program synthesis pipelines.
Application Category
📝 Abstract
Many techniques in program synthesis, superoptimization, and array programming require parallel rollouts of general-purpose programs. GPUs, while capable targets for domain-specific parallelism, are traditionally underutilized by such workloads. Motivated by this opportunity, we introduce a pleasingly parallel virtual machine and benchmark its performance by evaluating millions of concurrent array programs, observing speedups up to $147\times$ relative to serial evaluation.
Problem
Research questions and friction points this paper is trying to address.
program synthesis
superoptimization
array programming
GPU parallelism
parallel execution
Innovation
Methods, ideas, or system contributions that make the work stand out.
linear-algebraic hypervisor
pleasingly parallel virtual machine
GPU acceleration
array programming
program synthesis
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