PhD Defense - Simple DRAM and Virtual Memory Abstractions to Enable Highly Efficient Memory Subsystems

Vivek Seshadri gave his PhD defense today covering: how different memory resources have different granularities of access, and therefore need different management techniques.  These techniques come out of understanding what hardware could do, without necessarily identifying common features in existing applications that would require / benefit from these techniques.

Page Overlays / Overlay-on-Write: provide the ability to assign physical addresses at sub-page granularities (call them overlays).  This reduces the cost of sparse copy-on-writes.  In effect, assign a fresh sub-page unit and copy to that location.  On every access, check the overlay table in parallel to determine whether to use the normal translation or go to the overlay location.

Gather-Scatter DRAM: provide support for only requesting a subset of cachelines.  First, shuffle the data in a cacheline so that the same subset of multiple cache lines will map to different chips in DRAM.  Second, introduce additional logic (just a few gates) in DRAM that will compute a modified address, where the default pattern (stride 1) is the normal, un-modified access.  

RowClone + BuddyDRAM: can DRAM speedup memcpy (and other bulk memory operations)?  First, by opening one row after another, the bitline will take the initial value and then write it into another row.  More complex is opening multiple rows simultaneously, which results in bit-wise operations across the three rows: final = C (A | B) | ~C (A & B).  By controlling C, bulk bitwise operations are possible.  Using this technique, the system can exceed the memory bandwidth for these operations.

DirtyBlock Index: the problem is that if the source is dirty, then it needs to be written back before the previous techniques can be used.  DBI provides a faster lookup mechanism to determine if / where are any dirty block lines.

These techniques are interesting, but as the candidate noted, they are in effect solutions in search of a problem.  And with DRAM being commodity hardware, it is difficult to envision these techniques being adopted without further work.

Repost: How I spent my time at SIGCSE

When I attend a conference, I try to prepare blog posts detailing the presentations that I see and other items of objective content.  More important are the people in attendance.  I spend more time meeting with colleagues than I do actually sitting in the sessions.  Before you are shocked, understand that much of our conversation are about these sessions.  Depending on the conference there can be between 2 and 10? sessions occurring concurrently.  I cannot be in 2, let alone 10, places at once, so instead we sample (as in the appearance of randomly selecting) the sessions and then discuss.

I met Janet Davis, who now heads the CS program at Whitman College, during my first time at SIGCSE.  I value her different perspective and always try to seek her out at some point during the conference.

These themes merge in her blog post that shows some idea as to how each day is organized and how the sessions (papers, panels, etc) play a part in a very busy schedule.

Conference Attendance SIGCSE 2016 - Day 2

After lunch when we are all in food comas, let's attend the best paper talk!
A Multi-institutional Study of Peer Instruction in Introductory Computing -
This study followed 7 instructors across different institutions as they used peer instruction.  This showed that both the instruction is generally recognized as valuable, while also touching on routes in which it can go awry.  Tell students why this technique is being used and what it's effect.  Hard questions are good questions to ask, as students will discuss and learn from the question.  This requires that questions are graded for participation and not *correctness*.  Possible questions and material for peer instruction is available.

Development of a Concept Inventory for Computer Science Introductory Programming -
A concept inventory is a set of questions that carefully tease out student misunderstandings and misconceptions.  Take the exams and identify both the learning objective and the misconception that results in incorrect answers.

int addFiveToNumber(int n)
{
  int c = 0;
  // Insert line here
  return c;
}

int main(int argc, char** argv)
{
  int x = 0;
  x = addFiveToNumber(x);
  printf("%d\n", x);
  return 0;
}

a) scanf("%d", &n);
b) n = n + 5;
c) c = n + 5;
d) x = x + 5;

Each incorrect answer illustrates a different misconception.  For example, input must come from the keyboard.  Or variables are passed by reference.
Overall, this study illustrated how the concept inventory was developed, but not the impact of having it, or what it showed in the students and their learning.

Uncommon Teaching Languages - (specifically in intro courses)
An interesting effect of using an uncommon language in an introductory course is that the novices and experts have similar skills.  Languages should be chosen to minimize churn, otherwise students feel that they haven't mastered any languages.  And related to this point, languages also exist in an institutional ecosystem.  Furthermore, we want to minimize the keywords / concepts required for a simple program.  A novice will adopt these keywords, but they also are "magic" and arcane.  And then how long are the programs, as we want novices to only have to write short code to start.

I also attended the SIGCSE business meeting and then the NCWIT reception.  I have gone to NCWIT every year at SIGCSE, as I want to know what I should do (or not do) to not bias anyone's experience in Computer Science.

Conference Attendance SIGCSE 2016 - Day 1

Here I am at SIGCSE again.  This is a wonderful opportunity to think and reflect on how I assist students in learning Computer Science and to be Computer Scientists.  And to connect with other faculty, researchers, etc who are interested in teaching and doing so in a quality manner.

An Examination of Layers of Quizzing in Two Computer Systems Courses -
In this work, the instructor taught the Intro Computer Systems course and based on Bryant and O'Hallaron's book (paid link).  After several years of teaching, she introduced a new layer of quizzing to the course.  Effectively before each class, students take a pre-quiz worth ~0% of their grade (20 quizzes combine to 5%), and can then come to class with knowledge and feedback toward their deficiencies.  From the experience of the quizzes, students have been doing better in these courses.

Subgoals Help Students Solve Parsons Problems -  (previewed at Mark Guzdail's blog)
When learning new things, students benefit from labeling subgoals in solving.  These labels provide a basis for solving similar problems.  There are two different strategies for labeling: students can provide the labels or the assignment can provide the labels.  An example labeling can be found with loops: initialize, test, change.  If students provide the labels and provide cross-problem labels, they do best.  If they provide the labels and they are problem-specific such as "are there more tips" (with respect to an array of tips), then these students do worse than those provided the labels.  Developing labels can be valuable, but it may require the expert to still provide guidance to help abstract them across problems.  This talk had one of the great moments when someone asked a question and Brianna replied by, "So and so has done great ..."  And the questioner pointed out that he is "so and so".

As CS Enrollments Grow, Are We Attracting Weaker Students?: A Statistical Analysis of Student Performance in Introductory Programming Courses Over Time -
In this study, one instructor has analyzed the data of student assignment grades across 7 years of Fall semesters in the CS 1 course.  Several specific and clear reasonings were applied to get a clear and comparable data set.  The first test is that the number of student withdrawals remained the same as a percentage of the total class size.  The second test is that the means of the grades for the courses are statistically indistinguishable.  The third test is to use a mixture model (weighted combination of distributions) for each class's scores.  A good fit is found with two gaussian distributions, such that there is one for the "good students" and a second for the high variance students who are "potentially weaker".  From this, the study concluded that (at Stanford, in Fall CS1), there are more "weak students" and more "strong students" as the student enrollment is drawing from the same larger population.

A (Updated) Review of Empiricism at the SIGCSE Technical Symposium -
Using the proceedings from SIGCSE 14 and 15, they examined the empirical evaluation and the characteristics of these evaluations.  How was the data collected in each paper?  And what was being evaluated (pedagogy, assignments, tools, etc)?  Is the subject novel or replicating other studies?  Based on this study, would SIGCSE benefit from a separate track for longer paper submissions?  Or workshops on how to empirically validate results?  This and other material is being developed under an NSF grant and released publically.

Birds of a Feather -
In the evening, I attended two Birds of a Feather sessions.  Both of which have given me further ideas for what I might do to further (attempt to) improve student learning.  And also possible collaborators toward that end.