Automatic generation of software pipelines for heterogeneous parallel systems

Pienaar, J.; Chakradhar, Srimat; Raghunathan, Anand

doi:10.1109/sc.2012.22

Cited by 12 publications

(6 citation statements)

References 56 publications

(50 reference statements)

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“…Task graphs have been leveraged in numerous contexts including iteration scheduling for multicore processors [21,19], thread assignment for heterogenous cores [20,38], process scheduling for clusters [27,15], task scheduling for heterogeneous processors [40,15,26,44], etc. In the current form, directive-based languages allow scheduling and mapping of task graphs at runtime (as in StarPU and OmpSs), but do not facilitate application of task graph optimizations.…”

Section: Discussion and Related Workmentioning

confidence: 99%

Semi-automatic restructuring of offloadable tasks for many-core accelerators

Ravi

Yang

Bao

et al. 2013

Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

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Work division between the processor and accelerator is a common theme in modern heterogenous computing. Recent efforts (such as LEO and OpenAcc) provide directives that allow the developer to mark code regions in the original application from which offloadable tasks can be generated by the compiler. Auto-tuners and runtime schedulers work with the options (i.e., offloadable tasks) generated at compile time, which is limited by the directives specified by the developer. There is no provision for offload restructuring.We propose a new directive to add relaxed semantics to directive-based languages. The compiler identifies and generates one or more offloadable tasks in the neighbourhood of the code region marked by the directive. Central to our contribution is the idea of sub-offload and super-offload. In sub-offload, only a part of the code region marked by the developer is offloaded to the accelerator, while the other part executes on the CPU in parallel. This is done by splitting the index range of the main parallel loop into two or more parts and declaring one of the subloops as the offloadable task. Support is added to handle reduction variables and critical sections across subloops. Sub-offload enables concurrent execution of a task on the CPU and accelerator. In super-offload, a code region larger than the one specified by the developer is declared as the offloadable task (e.g., a parent loop). Super-offload reduces data transfers between CPU and accelerator memory.We develop Elastic Offload Compiler(EOC) for use alongside existing directive-based languages. The current implementation supports LEO for the new Intel Xeon Phi (MIC) architecture. We evaluate EOC with respect to SpecOMP and NAS Parallel Benchmarks. Speedups range between 1.3x-4.4x with the CPU version as baseline and 1.2x-24x with the offload (CPU-MIC) version as baseline.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. SC '13,

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Section: Discussion and Related Workmentioning

confidence: 99%

Semi-automatic restructuring of offloadable tasks for many-core accelerators

Ravi

Yang

Bao

et al. 2013

Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

Self Cite

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“…Directive-based Programming Models. These include technologies such as OpenMP, OpenACC [9] C ++ AMP (Microsoft), Spearmint [10], OP2 [11], in works [12][13] and DVM / DVMH [14][15][16].…”

Section: Existing Solutionsmentioning

confidence: 99%

“…Tasks communicate with each other by streams of data along the edges of the graph through queues using the producer-consumer scheme. TaskFlow has the ability to build heterogeneous graphs (using both CPU and GPU) and then pipelined execution similar to [13]. Such a computation model is promising since it can not only be efficiently performed on CPU and GPU, but also can be used for prototyping hardware implementations on FPGA or VLSI.…”

Section: Existing Solutionsmentioning

confidence: 99%

An Auto-Programming Approach to Vulkan

Frolov¹,

Sanzharov

Галактионов³

et al. 2021

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

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We propose a novel high-level approach for software development on GPU using Vulkan API. Our goal is to speed-up development and performance studies for complex algorithms on GPU, which is quite difficult and laborious for Vulkan due to large number of HW features low level details. The proposed approach uses auto programming to translate ordinary C++ to optimized Vulkan implementation with automatic shaders generation, resource binding and fine-grained barriers placement. Our model is not general-purpose programming, but is extendible and customer-focused. For a single C++ input our tool can generate multiple different implementations of algorithm in Vulkan for different cases or types of hardware. For example, we automatically detect reduction in C++ source code and then generate several variants of parallel reduction on GPU: with optimization for different warp size, with or without atomics, using or not subgroup operations. Another example is GPU ray tracing applications for which we can generate different variants: pure software implementation in compute shader, using hardware accelerated ray queries, using full RTX pipeline. The goal of our work is to increase productivity of developers who are forced to use Vulkan due to various required hardware features in their software but still do care about cross-platform ability of the developed software and want to debug their algorithm logic on the CPU. Therefore, we assume that the user will take generated code and integrate it with hand-written Vulkan code.

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“…Another case is StarPU [3], a library that offers runtime heterogeneity support and provides priority schedulers for task-to-processor allocation. AHP [22] is another framework that generates software pipelines for heterogeneous systems and schedules tasks to their earliest executor, based on profiling information gathered prior to runtime.…”

Section: Schedulers For Compute Acceleratorsmentioning

confidence: 99%

Criticality-Aware Dynamic Task Scheduling for Heterogeneous Architectures

Chronaki

Rico

Badía

et al. 2015

Proceedings of the 29th ACM on International Conference on Supercomputing

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Current and future parallel programming models need to be portable and efficient when moving to heterogeneous multicore systems. OmpSs is a task-based programming model with dependency tracking and dynamic scheduling. This paper describes the OmpSs approach on scheduling dependent tasks onto the asymmetric cores of a heterogeneous system. The proposed scheduling policy improves performance by prioritizing the newly-created tasks at runtime, detecting the longest path of the dynamic task dependency graph, and assigning critical tasks to fast cores. While previous works use profiling information and are static, this dynamic scheduling approach uses information that is discoverable at runtime which makes it implementable and functional without the need of an oracle or profiling. The evaluation results show that our proposal outperforms a dynamic implementation of Heterogeneous Earliest Finish Time by up to 1.15×, and the default breadth-first OmpSs scheduler by up to 1.3× in an 8-core heterogeneous platform and up to 2.7× in a simulated 128-core chip.

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Automatic generation of software pipelines for heterogeneous parallel systems

Cited by 12 publications

References 56 publications

Semi-automatic restructuring of offloadable tasks for many-core accelerators

Semi-automatic restructuring of offloadable tasks for many-core accelerators

An Auto-Programming Approach to Vulkan

Criticality-Aware Dynamic Task Scheduling for Heterogeneous Architectures

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