Tuesday, March 10, 2015

Review: Geek Sublime: The Beauty of Code, the Code of Beauty

A book of such promise is Geek Sublime: The Beauty of Code, the Code of Beauty, yet it fails.  The first third of this work followed the hopeful theme, intertwining the story of programming, its style, and the author's complex relationship with his Indian heritage, desire to be an artist (writer, etc), and his ability to make money programming.  An interesting twining that continued to encourage me to read, yet some worrisome signs developed.

The first worry was skipping a long section on how computers work.  This *is* my field of Computer Science, so I wasn't interested in reading the basics.  Yet worse was noticing some inaccuracies.  They established a certain level of understanding in Computer Science that was concerning, particularly in light of the author's non-technical background.  Livable, er readable, sure.

It was then the author's intent to show the great intellectual contributions made by Indians.  I have no dispute of this point, except that it wasn't actually fitting with the established theme of the work.  Finding that Sanskrit has a codified language of great antiquity enabled the author in this quest.  Alas, it was long pages that grew increasingly divorced with the first part of the title, "The Beauty of Code" and further focus on the later, "The Code of Beauty".  And this code deriving from Indian literary traditions.

In the end, the book concluded.  A crescendo of final claims that each read like next page would be the last, until such time as there was really no text left.  I learned something about Indian culture by reading this book, except that was not why I read it.  I did not gain in what I sought, so I cannot recommend reading it, nor care to provide a link to it.

Saturday, March 7, 2015

Conference Attendance SIGCSE 2015 - Day 2 / 3



I recognize that Day 1 afternoon went “missing”.  I presented my poster and that consumed the sum total of my time.  While I am happy with all that I achieved with my poster (writing IRB protocol, independent work, analyzing my teaching, et cetera), it was not considered as a finalist for the student research competition (SRC). Yet I received significant feedback and a number of follow-ons that I will have to try to evaluate the next time(s) I teach.  I have been doing an excellent job of networking and speaking with my colleagues.  And I have seen several exciting techniques to improve my teaching.

This was a small Bird of the Feather (BoF), but we discussed some approaches and things to know before attending your first conference, and not just for undergraduates.  However, in some cases, undergraduates are attending their local institution and may never have traveled any significant distance.  What do they really need to bring?




In traveling, take some time to prepare students.  Let them know what to expect.  For example, it is okay to miss some paper sessions, and even return to your room entirely.  It is okay to ask questions 1:1.  Find groups where people are being introduced and join in.  Student volunteering, while takes time, also gives an additional individuals that you will know.  Use the people you know to introduce you to others at the conference.

This is just what it sounds.  A Ruby based framework that enables writing simple unit tests that will then be applied to a full simulation of the assembly executed.

The presenter(s) were not at this poster, but it showed a high quality interface for seeing the scheduling of threads according to different scheduling policies.  The intent here was not to explore races and parallelism, but rather see how scheduling decisions are made in an OS.

I was not expecting this poster.  You are walking along and then see 4 Raspberry Pi's all networked together.  Raspberry Pis and HPC?!  A small setup, but it is an interesting development that takes advantage of the low cost Pi and still provide an HPC platform for students.

Plastic parts all worked together to form replicas of Pascal's mechanical calculator.  Interesting and student assembled.

Teams of 4 students, approach is evaluated on courses from three years of major (sophomore on up).  Teams are formed with CATME (particularly using dissimilar GPAs in a group), as well as partner selection (when possible).  Students provide peer evaluations after each stage of the project.  Significant data collection looking particularly at what students prefer for to be the evaluation policy (between 100% of grade for the group’s work to 100% of the grade for the individual’s contribution).  This question was taken repeatedly throughout the semester, which leads to whether student preferences change?  More senior students prefer more weight being attributed to group.  The predictor for what grade split is at what point in the course is this surveyed, and effectively as soon as the teams are formed the students prefer to be graded primarily as a group.  Follow on study is looking at experience with team projects, trust in the ability to evaluate individual contribution, and other questions.  This is a hopeful data point.

How do faculty become aware and why do they try out teaching practices?  66 participants in CS, including chairs, tenure-track faculty, teaching faculty, and Ph.D. student instructors across 36 institutions.  First, the mental model of what an instructor does can differ significantly from what the instructor is actually doing.  Second, faculty can find out about practices through a variety of approaches, such as self-identifying that there is possible improvement in their teaching.  Faculty often trust other faculty like them (researchers to researches, lecturers to lecturers).  Third, when adopting a practice, faculty need to evaluate the effectiveness (see also my poster, student feedback, etc).  -- My efforts in this have been having different faculty (my recommendation letter writers) view my lectures / teaching, and thereby giving them demonstrations of different practices.
"We lost the war on cheating"  Instead, we have to meet with students such that they are demonstrating their understanding of the code.  The requirements of submissions: attribute your sources and understand your submission.  Enables students to work together, use all sources, develop interview skills.  Enables reuse of assignments.  Grading is now 40% correctness / 60% code interview.  Rubric for each interview.  Students should arrive early and have their laptop ready to present / explain.  Students were better able to learn and complete the assignments, as well as feedback for improvement.  Students also felt better able to learn the material by being able to collaborate and not constrained by a collaboration policy.  There are some stressors, such as TAs having to meet with hundreds of students, as well as their inconsistencies.  -- This was perhaps the most exciting new technique that I saw / heard about.

Lunch time and closing speaker

Thursday, March 5, 2015

Conference Attendance SIGCSE 2015 - Day 1 Morning

It is colder here in Kansas City.  Fortunately, I will only be outside briefly.  Most often I will be networking and continuing my efforts to both become a better teacher, as well as finding an academic job teaching.

This morning, I am focusing on the "Curriculum" track.  I am excited by the three papers in this track, the first looks at research, the second is on systems courses, and the last on parallel computing courses.  Alas, I was in the hallway track and missed the first work.  Perhaps I can find the authors later.

Backward Design: An Integrated Approach to a Systems Curriculum
The goal of systems is "higher level software creation".  Computer Science courses are split into Core Tier 1 and Tier 2 (a term from the ACM 2013 curriculum), where the former are taken by all CS majors and the later are only taken by most or some.  One issue in the old curriculum was that OS also taught C.  In crafting a new curriculum, first establish a vision statement, which can be used in conflict resolution (and also revised).  Establish SMART objectives to prepare and build the assessments.  The results can be found on github.

A Module-based Approach to Adopting the 2013 ACM Curricular Recommendations on Parallel Computing
Parallel computing is important and important for CS graduates to know.  The 2013 ACM Curriculum increased the number of hours that students should take in parallel computing.  Part of the recommendations are to place parallel computing into the curriculum and not just as a course.  Thus parallelism modules are placed throughout the curriculum (perhaps as early as CS1 or CS2).  Find the level of abstraction for a concept and introduce it appropriately.  For example, Amdahl's Law in CS1 versus cache coherence in senior-level class.  5 modules of parallelism were established, which have equivalences with the ACM.  Each course in the curriculum may have 1 or more modules, which then teaches and reinforces the topics.  Even after adding these modules, there has continued to be incremental development and revisions, which have improved student outcomes.  The key take away is that it is possible to introduce these recommendations without completely rewriting the curriculum.

In the afternoon, I will be standing with my poster -  Using Active Learning Techniques in Mixed Undergraduate / Graduate Courses.  Later I will post updates from my afternoon.

Monday, February 23, 2015

Compilers and Optimizations, should you care?

Compiler optimizations matter.  One time in helping a fellow Ph.D. student improve a simulator's performance, I did two things: first, I replaced an expensive data structure with a more efficient one.  Second, I turned on compiler optimizations.  Together, the simulator ran 100x faster.

A question posted in the stackexchange system asked, "Why are there so few C compilers?"  The main answer pointed out that any C compiler needs to be optimizing.  Lots of optimizations are occurring on every compilation, and each one gaining tiniest increments in performance.  While I enjoy discussing them in detail, I generally wave my hands and tell of how they are good, yet make debugging difficult.  These optimizations are lumped together as the "optimization level".

In "What Every Programmer Should Know About Compiler Optimizations", we revisit optimizations.  First, the compiler is no panacea and cannot correct for inefficient algorithms or poor data structure choices (although I am party to a research on the later).  The article then suggests four points to assist the compiler in its efforts at optimizing the code. 

"Write understandable, maintainable code."  Please do this!  Usually, the expensive resource is the programmer.  So the first optimization step is to improve the programmer's efficiency with the source code.  Remember Review: Performance Anti-patterns and do not start optimizing the code until you know what is slow.

"Use compiler directives."  Scary.  Excepting the inline directive, I have used these less than a half dozen times in almost as many years of performance work.  Furthermore, the example of changing the calling convention is less relevant in 64-bit space where most conventions have been made irrelevant.

"Use compiler-intrinsic functions."  (see Compiler Intrinsics the Secret Sauce)  This can often dovetail with the first point by removing ugly bit twiddling code and putting in clean function calls.

"Use profile-guided optimization (PGO)."  This optimization is based on the dynamic behavior of the program.  Meaning that if you take a profile of the program doing X, and later the program does Y; executing Y can be slower.  The key is picking good, representative examples of the program's execution.

So you have dialed up the optimization level, and written understandable code sprinkled with intrinsics.  Now what?  The next step (with which I agree) is to use link time optimizations (LTO) / Link-Time Code Generation (LTCG).  This flag delays many optimizations until the entire program is available to be linked.  One of the principles of software performance is that the more of the program available to be optimized, the better it can be optimized.  (This principle also applies in computer architecture).  Thus, by delaying many optimization until the entire program is available, the linker can find additional and better opportunities than were present in individual components.

The article notes, "The only reason not to use LTCG is when you want to distribute the resulting object and library files."  And alas, I have fought several battles to overcome this point, as my work requires the use of LTO.  Perhaps in the next decade, LTO will be standard.

Wednesday, February 18, 2015

Going ARM (in a box)

ARM, that exciting architecture, is ever more available for home development.  At first, I was intrigued by the low price points of a Raspberry Pi.  And I have one, yet I felt the difficulties of three things: the ARMv6 ISA, the single core, and 512MB of RAM.  For my purposes, NVIDIA's development board served far better.  At present, that board is no longer available on Amazon; however, I have heard rumors of a 64-bit design being released soon.

With the release of the Raspberry Pi 2, many of my concerns have been allayed.  I am also intrigued by the possibility it offers of running Windows 10.

Monday, December 8, 2014

Improving Computer Science Education

Recently in the news are two articles that relate to improving Computer Science Education.  It is valuable to broaden the base of students who understand the basics of computing, just as students are expected to know Chemistry or Calculus.  In fact in my biased opinion, knowing the basics of computing and programming will have greater practical benefit to students; however, it should never be at the expense of a diverse education.

The White House announced that the 7 largest school districts will be including Computer Science in their curriculum.  This will quickly lead to another problem of who will teach the Computer Science classes.  Not me, but I am interested in teaching the teachers.  I do want to see Computer Science as an actual specialty (endorsement) for Education majors.

Another aspect of broadening the base is retaining students enrolled in the major.  Being part of the majority, it is difficult for me to know the challenges faced by other groups.  Similarly, I know why I entered Computer Science, so I would like to understand why others have too.  Why are they passionate or interested in this field that I am a part of?  Here are some things minority students have to say about STEM.

Wednesday, November 26, 2014

Computer Science Diversity

I attended a top program in Computer Science, where the gender split was 60 / 40.  Then I worked for five years at a major company.  Therefore, my expectation is always that anyone regardless of their gender, race, etc can succeed in Computer Science.  Now, recently there was a short lived Barbie book about being a computer engineer.  Ignoring any semantics about Computer Science, Software Engineering, and Computer Engineering being different disciplines, the work still did not portray women in the more technical efforts.  I'd rather read a collegue's remix of the work.

In a different vein of diversity, as a white male, I have been regularly been excluded from tech events because I dislike the taste of alcohol.  Thus at the (especially) frequent events in industry settings where alcohol is served, I was not socializing with my colleagues, and instead would inevitably find myself back at my desk working.  As a consequence, I was effectively excluded from the event.  And now in academia, I find myself attending conferences, where the social events are also touted for serving alcohol.  I have no issue with serving alcohol, rather it is the near exclusivity of which the drink options trend that way.  Thus a recent article struck a chord in the continuing desirability of extending the options and respecting the decision (for whatever reason) to not drink alcohol.