This document discusses optimizations for Java programs to better utilize CPUs, especially newer CPU instructions. It covers how Java code is compiled to bytecode then JIT compiled to machine code at runtime. Improvements in OpenJDK 9-11 are highlighted, including support for Intel AVX-512, fused multiply-add, SHA extensions, and reducing penalties when switching between instruction sets. Optimizing math functions and string processing with SIMD is also discussed.
Y Combinator 餫及3煦疋永民氾件皿伊奈玄匹允﹝場ぶ及旦正奈玄失永皿卞反眕狟及傖毛云嶀嶀楔々中牏飽
1. Problem
2. Solution
3. Market Size
4. Traction
5. Unique Insight
6. Business Model
7. Team
UTokyo 500k 蚚及氾件皿伊奈玄午仄化釬傖仄引仄凶﹝
犯奈正穴奶瓦件弘支C迮悝毛支月午五卞方仁觳午卅月☆伉奈弗奈斥★毛滅什源楊卞勾中化元凶恅☆Leakage in Data Mining: Formulation, Detecting, and Avoidance★(Kaufman, Shachar, et al., ACM Transactions on Knowledge Discovery from Data (TKDD) 6.4 (2012): 1-21.)毛賤掊仄引允﹝
翋卅囀搕玾婘瞻峇云曰匹允﹝
?綎奶侔薴迨縞磡`弗奈斥及岈瞰及畿賡
?伉奈弗奈斥毛滅什凶戶及2勾及蕉尹源
?伉奈弗奈斥及逃
?伉奈弗奈斥及党淏
This document discusses optimizations for Java programs to better utilize CPUs, especially newer CPU instructions. It covers how Java code is compiled to bytecode then JIT compiled to machine code at runtime. Improvements in OpenJDK 9-11 are highlighted, including support for Intel AVX-512, fused multiply-add, SHA extensions, and reducing penalties when switching between instruction sets. Optimizing math functions and string processing with SIMD is also discussed.
Y Combinator 餫及3煦疋永民氾件皿伊奈玄匹允﹝場ぶ及旦正奈玄失永皿卞反眕狟及傖毛云嶀嶀楔々中牏飽
1. Problem
2. Solution
3. Market Size
4. Traction
5. Unique Insight
6. Business Model
7. Team
UTokyo 500k 蚚及氾件皿伊奈玄午仄化釬傖仄引仄凶﹝
犯奈正穴奶瓦件弘支C迮悝毛支月午五卞方仁觳午卅月☆伉奈弗奈斥★毛滅什源楊卞勾中化元凶恅☆Leakage in Data Mining: Formulation, Detecting, and Avoidance★(Kaufman, Shachar, et al., ACM Transactions on Knowledge Discovery from Data (TKDD) 6.4 (2012): 1-21.)毛賤掊仄引允﹝
翋卅囀搕玾婘瞻峇云曰匹允﹝
?綎奶侔薴迨縞磡`弗奈斥及岈瞰及畿賡
?伉奈弗奈斥毛滅什凶戶及2勾及蕉尹源
?伉奈弗奈斥及逃
?伉奈弗奈斥及党淏
WebRTC Conference Japan 2016 (2016爛2堎16) 及琌栳揃蹋匹允﹝
逃桶氪反笢佡婭午湮踩嗷謠硝 http://www.slideshare.net/rotsuya 匹允﹝
※Telexistence Robot controlled with WebRTC§
It's the presentation slides at WebRTC Conference Japan on Feb 16, 2016.
The presenters were Toshiya Nakakura and Ryosuke Otsuya http://www.slideshare.net/rotsuya .
The document appears to be a presentation from the Developers Summit 2019 hosted by DENSO Corporation. It discusses DENSO's initiatives in IT and digital innovation. The presentation was given by Yoshiei Sato and Susumu Tomita from DENSO's Digital Innovation, Engineering Research & Development department. The document contains technical details and diagrams related to software development, data processing, and connected vehicle technologies.
This document discusses Amazon S3 and Glacier storage services. It provides an overview of S3 and Glacier, including how they are used to store and retrieve objects, their scalability and availability features, and pricing and billing models. The document also compares S3 and Glacier and how they are suited for different storage needs based on access frequency and cost.
This document summarizes a presentation on machine learning given by Masaki Samejima at the 2019 Developers Summit. The presentation covered topics including computer vision models and frameworks, model serving, AutoML, and hardware for machine learning. Key frameworks discussed were MXNet, Gluon, PyTorch, TensorFlow and ONNX. The document also provided examples of computer vision tasks like classification, detection and segmentation as well as generative models.
This document discusses gumi's infrastructure and services. It describes moving from 20 app servers to 90, scaling out Aurora from 3 to 11 instances, and increasing Redis instances from 1 to 14. The document also outlines gumi's approach to using AWS services like S3, CloudFront, Aurora, and Redis across public, private and management network segments.