diff --git a/config.toml b/config.toml index ed705e1..c904b58 100644 --- a/config.toml +++ b/config.toml @@ -127,20 +127,20 @@ defaultContentLanguageInSubdir = false # icon_pack = "fa" # link = "mailto:test@example.org" - [[params.social]] - icon = "twitter" - icon_pack = "fa" - link = "//twitter.com/GeorgeCushen" - [[params.social]] icon = "google-scholar" icon_pack = "ai" - link = "https://scholar.google.com/citations?user=K2nzqpYAAAAJ" + link = "//scholar.google.com/citations?user=K2nzqpYAAAAJ" [[params.social]] icon = "github" icon_pack = "fa" - link = "https://github.com/cwpearson" + link = "//github.com/cwpearson" + + [[params.social]] + icon = "linkedin" + icon_pack = "fa" + link = "//www.linkedin.com/in/carl-pearson-b133733a/" # Navigation Links diff --git a/content/home/about.md b/content/home/about.md index 6244c72..28f151d 100644 --- a/content/home/about.md +++ b/content/home/about.md @@ -28,7 +28,7 @@ weight = 1 # Biography -I am a PhD candidate in the Electrical and Computer Engineering department at the University of Illinois at Urbana-Champaign and a member of the IMPACT Research Group led by Wen-Mei Hwu. +I am a PhD candidate in the Electrical and Computer Engineering department at the University of Illinois at Urbana-Champaign and a member of the [IMPACT Research Group](http://impact.crhc.illinois.edu/) led by Wen-Mei Hwu. I am working on developing CAB (Cognitive Application Builder) as part of the joint UIUC / IBM C3SR cognitive computing systems research center. CAB will allow cognitive application developers to quickly write and debug high-performance scalable cogntitive computing applications. diff --git a/content/project/app_studies.md b/content/project/app_studies.md index fadf584..19c5a3b 100644 --- a/content/project/app_studies.md +++ b/content/project/app_studies.md @@ -14,6 +14,9 @@ tags = ["impact"] +++ -IME-Accelerators +I am part of the IME-Accelerators team for the NCSA PAID program. This program pairs domain scientists with computing specialists to improve effective utilization of the Blue Waters supercomputer at the National Petascale Computing Facility. I have worked on: -Huawei \ No newline at end of file + - CUDA Kernel Optimization for [AWP](https://scec.usc.edu/scecpedia/AWP-ODC) with the Southern California Earthquake Center + - OpenACC Acceleration for [SETSM](https://u.osu.edu/setsm/) with Ohio State University + +I am also working on applying tools developed by the IMPACT research group to high-performance code in collaboration with Huawei. \ No newline at end of file diff --git a/content/project/cab.md b/content/project/cab.md index f4929ec..14ebe14 100644 --- a/content/project/cab.md +++ b/content/project/cab.md @@ -2,14 +2,16 @@ highlight = true external_link = "" image_preview = "" -summary = "The Cognitive Application Builder" +summary = "Cognitive Application Builder" tags = ["c3sr", "impact"] date = "2017-03-13T21:24:51-05:00" -title = "cab" +title = "Cognitive Application Builder" math = false [header] caption = "" image = "" -+++ \ No newline at end of file ++++ + +The Cognitive Application Builder (CAB) is part of the joint UIUC / IBM C3SR cognitive computing systems research center. CAB will allow cognitive application developers to quickly write and debug high-performance scalable cogntitive computing applications. \ No newline at end of file diff --git a/content/publication/2014chen.md b/content/publication/2014chen.md index 849b20d..037b438 100644 --- a/content/publication/2014chen.md +++ b/content/publication/2014chen.md @@ -5,12 +5,19 @@ date = "2014-01-01" title = "Adaptive Cache Bypass and Insertion for Many-Core Accelerators" authors = ["Xuhao Chen", "Shengzhao Wu", "Li-Wen Chang", "Wei-Sheng Huang", "Carl Pearson", "Wen-mei Hwu"] -abstract = "An abstract..." +abstract = 'Many-core accelerators, e.g. GPUs, are widely used for accelerating general-purpose compute kernels. +With the SIMT execution model, GPUs can hide memory latency through massive multithreading for many regular applications. +To support more applications with irregular memory access pattern, cache hierarchy is introduced to GPU architecture to capture input data sharing and mitigate the effect of irregular accesses. +However, GPU caches suffer from poor efficiency due to severe contention, which makes it difficult to adopt heuristic management policies, and also limits system performance and energy-efficiency. +We propose an adaptive cache management policy specifically for many-core accelerators. +The tag array of L2 cache is enhanced with extra bits to track memory access history, an thus the locality information is captured and provided to L1 cache as heuristics to guide its run-time bypass and insertion decisions. +By preventing un-reused data from polluting the cache and alleviating contention, cache efficiency is significantly improved. +As a result, the system performance is improved by 31% on average for cache sensitive benchmarks, compared to the baseline GPU architecture.' image = "" image_preview = "" math = false -publication = "The publishing part of the citation goes here. You may use *Markdown* for italics etc." +publication = "*Proceedings of International Workshop on Manycore Embedded Systems.* ACM, 2014." url_code = "" url_dataset = "" diff --git a/content/publication/2016dakkak.md b/content/publication/2016dakkak.md index 3409cf5..61877ef 100644 --- a/content/publication/2016dakkak.md +++ b/content/publication/2016dakkak.md @@ -5,7 +5,13 @@ date = "2016-01-01" title = "WebGPU: A Scalable Online Development Platform for GPU Programming Courses" authors = ["Adbul Dakkak", "Carl Pearson", "Cheng Li"] -abstract = "The popularity of computer science classes offered through Massive Open On-line Courses (MOOCs) creates both opportunities and challenges. Programming-based classes need to provide consistent development infrastructures that are both scalable and user friendly to students. The \"Heterogeneous Parallel Programming\" class offered through Coursera teaches GPU programming and encountered these problems. We developed WebGPU - an online GPU development platform - providing students with a user friendly scalable GPU computing platform throughout the course. It has been used as the CUDA, OpenACC, and OpenCL programming environment for large Coursera courses, short-running summer schools, and traditional semester-long graduate and undergraduate courses. WebGPU has since replaced our traditional development infrastructure for the GPU classes offered at UIUC. This paper presents the original, revised, and upcoming WebGPU designs that address the requirements and challenges of offering sophisticated computing resources to a large, quickly-varying number of students." +abstract = 'The popularity of computer science classes offered through Massive Open On-line Courses (MOOCs) creates both opportunities and challenges. +Programming-based classes need to provide consistent development infrastructures that are both scalable and user friendly to students. +The \"Heterogeneous Parallel Programming\" class offered through Coursera teaches GPU programming and encountered these problems. +We developed WebGPU - an online GPU development platform - providing students with a user friendly scalable GPU computing platform throughout the course. +It has been used as the CUDA, OpenACC, and OpenCL programming environment for large Coursera courses, short-running summer schools, and traditional semester-long graduate and undergraduate courses. +WebGPU has since replaced our traditional development infrastructure for the GPU classes offered at UIUC. +This paper presents the original, revised, and upcoming WebGPU designs that address the requirements and challenges of offering sophisticated computing resources to a large, quickly-varying number of students.' image = "" image_preview = ""