Most undergraduate computer science programs still treat core developer tools including the command-line interface (CLI), symbolic debuggers, and version control systems as skills students will acquire on their own outside of class. We believe that these skills deserve the same explicit instruction, practice, and assessment that computer science programs dedicate to core topics such as data structures and algorithms.

This paper introduces NeCTAr (Near-Cache Transformer Accelerator), a 16nm heterogeneous multicore RISC-V SoC for sparse and dense machine learning kernels with both near-core and near-memory accelerators. A prototype chip runs at 400MHz at 0.85V and performs matrix-vector multiplications with 109 GOPs/W. The effectiveness of the design is demonstrated by running inference on a sparse language model, ReLU-Llama.

In recent years, a diverse body of research in computing education has discussed new pedagogies and curriculum changes to improve learning and students' experiences. Topics such as growth mindset, mastery learning, grading for equity, and specifications grading are important steps towards the Holy Grail: "A's for All" (as time and interest allow). In this new teaching approach, the "A" line does not move, but instead every student is given an opportunity to achieve proficiency and earn it, as long as they are willing to put in the time and effort it takes. In other words, students can all achieve the same learning outcomes at a different pace, instead of the traditional approach where students achieve different learning outcomes in a fixed amount of time. This workshop will provide educators and administrators with tools to implement the "A's for All" (as time and interest allow) approach in their courses and institutions. It will take attendees through elements of advocacy, hands-on randomized question generator design and implementation using a computer-based assessment system, best practices, and course policies to reduce friction.

The SIGCSE-MEMBERS mailing list of the ACM Special Interest Group in Computer Science Education is the main forum for educators worldwide to discuss computing education research, pedagogy, and curriculum. In early 2022 it was abuzz with several connected movements: growth mindset, proficiency (aka mastery) learning, grading for equity, and specifications grading. Each of these is an important step toward the Holy Grail: A's for All (as time and interest allow); the "A" line doesn't move, but every student should be given an opportunity to achieve proficiency and earn it, as long as they are willing to put in the time and effort it might take. The mantra is not "fixed time, variable learning", but "fixed learning, variable time". The goal of this full-day workshop is to provide educators and administrators with tools to achieve it in their courses and institutions.

The SIGCSE-MEMBERS mailing list of the ACM Special Interest Group in Computer Science Education is the main forum for educators worldwide to discuss computing education research, pedagogy, and curriculum. In early 2022 it was abuzz with several connected movements: growth mindset, proficiency (aka mastery) learning, grading for equity, and specifications grading. Each of these is an important step toward the Holy Grail: A's for All (as time and interest allow); the "A" line doesn't move, but every student should be given an opportunity to achieve proficiency and earn it, as long as they are willing to put in the time and effort it might take. The mantra is not "fixed time, variable learning", but "fixed learning, variable time".

This paper provides an experience report for the asynchronous online assessments in two classes in Fall 2020: CS0 and CS3. The two courses shared many structural similarities; both were taught by the same instructor, with three exams delivered asynchronously using the same submission and question randomization software. They differed in proctoring rules, time allowances, student feedback, and lessons learned. In both classes, the first exam followed the same format, with students being given unlimited time over a 24-hour period to take the exam; later exams diverged due to different preferences among students in each class.

Serverless is a popular choice for cloud service architects because it can provide scalability and load-based billing with minimal developer effort. Functions-as-a-service (FaaS) are originally stateless, but emerging frameworks add stateful abstractions. For instance, the widely used Durable Functions (DF) allow developers to write advanced serverless applications, including reliable workflows and actors, in a programming language of choice. DF implicitly and continuosly persists the state and progress of applications, which greatly simplifies development, but can create an IOps bottleneck. To improve efficiency, we introduce Netherite, a novel architecture for executing serverless workflows on an elastic cluster. Netherite groups the numerous application objects into a smaller number of partitions, and pipelines the state persistence of each partition. This improves latency and throughput, as it enables workflow steps to group commit, even if causally dependent. Moreover, Netherite leverages FASTER's hybrid log approach to support larger-than-memory application state, and to enable efficient partition movement between compute hosts. Our evaluation shows that (a) Netherite achieves lower latency and higher throughput than the original DF engine, by more than an order of magnitude in some cases, and (b) that Netherite has lower latency than some commonly used alternatives, like AWS Step Functions or cloud storage triggers.

With many Universities and Institutions moving toward online learning during the pandemic, content delivery and assessment become a new challenge for many educators. It comes as no surprise that many tools aimed at delivering computer-based assessments have become popular and are fields of active research. Members of the ACE Lab at UC Berkeley are creating question generators for computer science classes on a platform called PrairieLearn, to generate different randomized variants of questions. This poster discusses our team's approach to question variant categorization, which we believe would be useful metadata for other educators reusing our questions or generating their own.

Serverless, or Functions-as-a-Service (FaaS), is an increasingly popular paradigm for application development, as it provides implicit elastic scaling and load based billing. However, the weak execution guarantees and intrinsic compute-storage separation of FaaS create serious challenges when developing applications that require persistent state, reliable progress, or synchronization. This has motivated a new generation of serverless frameworks that provide stateful abstractions. For instance, Azure's Durable Functions (DF) programming model enhances FaaS with actors, workflows, and critical sections. As a programming model, DF is interesting because it combines task and actor parallelism, which makes it suitable for a wide range of serverless applications. We describe DF both informally, using examples, and formally, using an idealized high-level model based on the untyped lambda calculus. Next, we demystify how the DF runtime can (1) execute in a distributed unreliable serverless environment with compute-storage separation, yet still conform to the fault-free high-level model, and (2) persist execution progress without requiring checkpointing support by the language runtime. To this end we define two progressively more complex execution models, which contain the compute-storage separation and the record-replay, and prove that they are equivalent to the high-level model.

Much research has shown that social media platforms have substantial population biases. However, very little is known about how these population biases affect the many algorithms that rely on social media data. Focusing on the case study of geolocation inference algorithms and their performance across the urban-rural spectrum, we establish that these algorithms exhibit significantly worse performance for underrepresented populations (i.e. rural users). We further establish that this finding is robust across both text- and network-based algorithm designs. However, we also show that some of this bias can be attributed to the design of algorithms themselves rather than population biases in the underlying data sources. For instance, in some cases, algorithms perform badly for rural users even when we substantially overcorrect for population biases by training exclusively on rural data. We discuss the implications of our findings for the design and study of social media-based algorithms.