Portfolio
This page includes some of the technical work I did, mostly as an individual contributor to the Chapel programming language. You can find very short description of each of the work and link to the initial PR that added the work to the Chapel code base.
December 3, 2025
Mason is Chapel's package manager. This pull request enabled Mason projects to use code implemented in any other language that interoperates with the actual Chapel code.
Support for Building External Code in Mason Packages: Prerequisites
Pull RequestMason is Chapel's package manager. This pull request enabled Mason projects to use code implemented in any other language that interoperates with the actual Chapel code.
July 8, 2024
Slices of Chapel arrays, local or distributed, can be copied as 'Arr1[x..y] = Arr2[a..b];'. However, this pattern had some overheads. With this work those overheads are completely removed from this pattern, making it as fast as other, more verbose alternatives under many circumstances.
Array View Elision (AVE): Improving Common Array Slicing Patterns
Pull RequestSlices of Chapel arrays, local or distributed, can be copied as 'Arr1[x..y] = Arr2[a..b];'. However, this pattern had some overheads. With this work those overheads are completely removed from this pattern, making it as fast as other, more verbose alternatives under many circumstances.
April 25, 2024
Chapel has rich support for reductions, where users can sum-reduce an array with '+ reduce MyArr'. This work enabled that support for GPU-stored arrays as well. It involved wiring the Chapel compiler to generate GPU kernels that can use CUB and hipCUB, through some runtime support
Reductions for GPU-based Arrays
Pull RequestChapel has rich support for reductions, where users can sum-reduce an array with '+ reduce MyArr'. This work enabled that support for GPU-stored arrays as well. It involved wiring the Chapel compiler to generate GPU kernels that can use CUB and hipCUB, through some runtime support
May 12, 2023
With fundamentals of Chapel's GPU support in place, this added a new mode such that GPU-targeted applications can be developed without actual GPUs present, lowering the barrier for GPU programming even further.
Enabling Chapel's GPU Support without GPUs
Pull RequestWith fundamentals of Chapel's GPU support in place, this added a new mode such that GPU-targeted applications can be developed without actual GPUs present, lowering the barrier for GPU programming even further.
August 4, 2021
This is the work that broke ground for adding GPU support to the Chapel compiler. While there was some support for very low-level operations, after this PR, order-independent Chapel loops are launched as GPU kernels.
Initial Support for Automatically Generating GPU Kernels
Pull RequestThis is the work that broke ground for adding GPU support to the Chapel compiler. While there was some support for very low-level operations, after this PR, order-independent Chapel loops are launched as GPU kernels.
January 25, 2021
Building on some other optimizations in the Chapel compiler and standard modules, AA automatically transforms some data-parallel 'forall' loops to use aggregation without any user involvement.
Automatic Aggregation (AA): Remote Access Aggregation using Compiler Optimization and CopyAggregation
Pull RequestBuilding on some other optimizations in the Chapel compiler and standard modules, AA automatically transforms some data-parallel 'forall' loops to use aggregation without any user involvement.
September 21, 2020
With this work, data can be copied in parallel between two Chapel arrays without any user intervention. Effectively, this results in Chapel array copies to be faster than 'memcpy' operation in C, as multiple cores are used for copying the data, effectively saturating the memory bandwidth
Parallel Array Assignments
Pull RequestWith this work, data can be copied in parallel between two Chapel arrays without any user intervention. Effectively, this results in Chapel array copies to be faster than 'memcpy' operation in C, as multiple cores are used for copying the data, effectively saturating the memory bandwidth
August 23, 2020
Domains are index sets on which arrays can be defined. This is an optimization to make array creation much faster when their domains are known to be constant. As a result, array creation got ~2x faster, whereas array-of-array creation got ~6x faster in some cases.
Constant Domain Optimization
Pull RequestDomains are index sets on which arrays can be defined. This is an optimization to make array creation much faster when their domains are known to be constant. As a result, array creation got ~2x faster, whereas array-of-array creation got ~6x faster in some cases.
June 15, 2020
This work automatically optimizes Chapel's data-parallel 'forall' loops such that many distributed array accesses are performed much faster. This relies on a brand new compiler analysis that leverages Chapel's first-class parallelism and locality features.
Automatic Local Access (ALA): Making Local Accesses to Distributed Arrays Faster with a Compiler Optimization
Pull RequestThis work automatically optimizes Chapel's data-parallel 'forall' loops such that many distributed array accesses are performed much faster. This relies on a brand new compiler analysis that leverages Chapel's first-class parallelism and locality features.
July 30, 2019
This work added a 'bytes' type to Chapel. Heavily inspired by Python's 'bytes' type, this enabled us to switch UTF-8 validation on for Chapel's 'string' type, where 'bytes' can be used for non-textual data
Implementing 'bytes' Type in Chapel
Pull RequestThis work added a 'bytes' type to Chapel. Heavily inspired by Python's 'bytes' type, this enabled us to switch UTF-8 validation on for Chapel's 'string' type, where 'bytes' can be used for non-textual data
August 16, 2016
This was one of my internship projects at Cray Inc. I added a new pass to the Chapel compiler that reduced the complexity and size of the generated code, greatly improving developer productivity.
New Compiler Pass: Denormalize
Pull RequestThis was one of my internship projects at Cray Inc. I added a new pass to the Chapel compiler that reduced the complexity and size of the generated code, greatly improving developer productivity.
June 6, 2016
Chapel's sparse domains represent sparse index sets that can be used to create sparse arrays. Non-zero indices could be added to sparse domains, however, that resulted in element-wise addition. This work enabled adding elements in bulk, significantly improving sparse index population performance.
Fast Bulk Index Addition for Sparse Domains
Pull RequestChapel's sparse domains represent sparse index sets that can be used to create sparse arrays. Non-zero indices could be added to sparse domains, however, that resulted in element-wise addition. This work enabled adding elements in bulk, significantly improving sparse index population performance.